14.5.7.3 Neural Networks for Classification and Pattern Recognition

Chapter Contents (Back)
Neural Networks. Classification. See also Neural Networks for Segmentation.

Caianiello, E.R., Grimson, W.E.L.,
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BioCyber(22), 1976, pp. 1-6. BibRef 7600

Caianiello, E.R., Grimson, W.E.L.,
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PR(10), No. 1, 1978, pp. 27-30.
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BibRef

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PR(15), No. 6, 1982, pp. 455-469.
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This algorithm can be realized with a multilayered network consisting of neuron-like cells. BibRef

Lippmann, R.P.[Richard P.],
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CommunMag(27), No. 11, November, 1989, pp. pp. 47-54. Prototype based classifier faster than gradient descent. BibRef 8911

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TNN(3), No. 1, January 1992, pp. xx. BibRef 9201

Gupta, L.[Lalit], Wang, J.S.[Jie-Sheng], Charles, A.[Alain], Kisatsky, P.[Paul],
Prototype selection rules for neural network training,
PR(25), No. 11, November 1992, pp. 1401-1408.
WWW Link. 0401
BibRef

Shih, F.Y.[Frank Y.], Moh, J.[Jenlong], Chang, F.C.[Fu-Chun],
A new art-based neural architecture for pattern classification and image enhancement without prior knowledge,
PR(25), No. 5, May 1992, pp. 533-542.
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Chou, W.S.[Wen-Shou], Chen, Y.C.[Yung-Chang],
A new fast algorithm for effective training of neural classifiers,
PR(25), No. 4, April 1992, pp. 423-429.
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Yeung, D.Y.[Dit-Yan],
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PR(26), No. 1, January 1993, pp. 189-204.
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BibRef

Kim, H.J.[Ho J.], Yang, H.S.[Hyun S.],
A Neural-Network Capable of Learning and Inference for Visual-Pattern Recognition,
PR(27), No. 10, October 1994, pp. 1291-1302.
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Pham, D.T., Bayro-Corrochano, E.J.,
Self-organizing neural-network-based pattern clustering method with fuzzy outputs,
PR(27), No. 8, August 1994, pp. 1103-1110.
WWW Link. 0401
BibRef

Abou-Nasr, M.A., Sid-Ahmed, M.A.,
Fast learning and efficient memory utilization with a prototype based neural classifier,
PR(28), No. 4, April 1995, pp. 581-593.
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prototype classifiers learn faster than gradient descent methods. BibRef

Moh, J.[Jenlong], Shih, F.Y.[Frank Y.],
A general purpose model for image operations based on multilayer perceptrons,
PR(28), No. 7, July 1995, pp. 1083-1090.
WWW Link. 0401
BibRef

Wang, L.P.[Li-Po], Alkon, D.L.[Daniel L.],
An artificial neural network system for temporal-spatial sequence processing,
PR(28), No. 8, August 1995, pp. 1267-1276.
WWW Link. 0401
BibRef

Tumer, K.[Kagan], Ghosh, J.[Joydeep],
Analysis of decision boundaries in linearly combined neural classifiers,
PR(29), No. 2, February 1996, pp. 341-348.
WWW Link. 0401
BibRef
Earlier:
Estimating the Bayes Error Rate Through Classifier Combining,
ICPR96(II: 695-699).
IEEE DOI 9608
(Univ. of Texas, Austin, USA) BibRef

Jun, G.[Goo], Ghosh, J.[Joydeep],
Nearest-Manifold Classification with Gaussian Processes,
ICPR10(914-917).
IEEE DOI 1008
BibRef

Ravichandran, A., Yegnanarayana, B.,
Studies on Object Recognition from Degraded Images Using Neural Networks,
NeurNet(8), No. 3, 1995, pp. 481-488. BibRef 9500

Graf, H.P., Nohl, C.R., Ben, J.,
Image Recognition with an Analog Neural-Net Chip,
MVA(8), No. 2, 1995, pp. 131-140. BibRef 9500
Earlier: ICPR92(IV:11-14).
IEEE DOI 9208
BibRef

Lin, W.G.[Wen-Gou], Wang, S.S.[Shuenn-Shyang],
A Modified S-Neuron and Its Application to Scale-Invariant Classification,
PR(28), No. 9, September 1995, pp. 1423-1432.
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Gazula, S.[Srinivas], Kabuka, M.R.[Mansur R.],
Design of Supervised Classifiers Using Boolean Neural Networks,
PAMI(17), No. 12, December 1995, pp. 1239-1246.
IEEE DOI BibRef 9512
And: A2 only:
Reply to: Comments on 'Design of Supervised Classifiers Using Boolean Neural Networks',
PAMI(21), No. 9, September 1999, pp. 957-958.
IEEE DOI See also Comments on Design of Supervised Classifiers Using Boolean Neural Networks. BibRef

Hussain, B., Kabuka, M.R.,
A novel feature recognition neural network and its application to character recognition,
PAMI(16), No. 1, January 1994, pp. 98-106.
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BibRef

Smith, G.,
Comments on 'Design of Supervised Classifiers Using Boolean Neural Networks',
PAMI(21), No. 9, September 1999, pp. 956.
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Ishii, S., Fukumizu, K., and Watanabe, S.,
A net work of chaotic elements for information processing,
NeurNet(9), No. 1, January 1996, pp. 25-40.
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Grimes, C., Picton, P.D., Elliman, D.G.,
A Neural-Network Position-Independent Multiple Pattern Recogniser,
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Elliman, D.G.[David G.], Youssef, S.M.[Sherin M.],
Contextual Swarm-Based Multi-layered Lattices: A New Architecture for Contextual Pattern Recognition,
DAS04(496-507).
Springer DOI 0505
BibRef

di Zenzo, S.[Silvano],
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IVC(1), No. 2, May 1983, pp. 93-97.
WWW Link. 0401
BibRef

di Zenzo, S., Burgess, N., Ferragina, P., Granieri, M.N.,
Recognition by Constructive Neural Algorithms,
PRL(14), No. 12, December 1993, pp. 997-1007. BibRef 9312

Burgess, N., Granieri, M.N.,
A growing network classifier of 3D objects using multiple views,
ICPR92(II:512-515).
IEEE DOI 9208
BibRef

Ridella, S., Rovetta, S., Zunino, R.,
Circular Backpropagation Networks for Classification,
TNN(8), No. 1, January 1997, pp. 84-97. 9701
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Kanaoka, T., Chellappa, R., Yoshitaka, M., Tomita, S.,
A Higher-Order Neural Network for Distortion Invariant Pattern Recognition,
PRL(13), 1992, pp. 837-841. BibRef 9200

Kaita, T.[Takeshi], Tomita, S.[Shingo], Yamanaka, J.[Junkichi],
On a Higher-Order Neural Network for Distortion Invariant Pattern Recognition,
PRL(23), No. 8, June 2002, pp. 977-984.
Elsevier DOI 0204
BibRef

Osman, H., Fahmy, M.M.,
On The Discriminatory Power Of Adaptive Feedforward Layered Networks,
PAMI(16), No. 8, August 1994, pp. 837-842.
IEEE DOI BibRef 9408

Osman, H., Fahmy, M.M.,
Neural Classifiers and Statistical Pattern-Recognition: Applications for Currently Established Links,
SMC-B(27), No. 3, June 1997, pp. 488-497.
IEEE Top Reference. 9706
BibRef

Hsu, T.C., Wang, S.D.,
The K1-Map Reduction for Pattern Classifications,
PAMI(19), No. 6, June 1997, pp. 616-622.
IEEE DOI 9708
An approach for Restricted Coulomb Energy (RCE) networks to determine the number of clusters or network centers. BibRef

Banarse, D.S., Duller, A.W.G.,
Deformation Invariant Visual Object Recognition: Experiments with a Self-Organizing Neural Architecture,
NeurCompApp(6), No. 2, 1997, pp. 79-90. 9801
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Ray, K.S., Ghoshal, J.,
Neuro Fuzzy Approach to Pattern-Recognition,
NeurNet(10), No. 1, January 1997, pp. 161-182. 9702
BibRef

Ray, K.S.[Kumar S.],
Pattern Recognition Based on Fuzzy Set and Genetic Algorithm,
IJIG(14), No. 03, 2014, pp. 1450009.
DOI Link 1410
BibRef

Ornes, C., Sklansky, J.,
A Neural-Network That Visualizes What It Classifies,
PRL(18), No. 11-13, November 1997, pp. 1301-1306. 9806
BibRef

Ornes, C., Sklansky, J.,
A Visual Neural Classifier,
SMC-B(28), No. 4, August 1998, pp. 620-625.
IEEE Top Reference. 9808
BibRef

Ornes, C.[Chester], Sklansky, J.[Jack], Disher, A.[Anthony],
A Visual Neural Network that Learns Perceptual Relationships,
ICPR98(Vol I: 873-875).
IEEE DOI 9808
BibRef

Auda, G., Kamel, M.,
CMNN: Cooperative Modular Neural Networks for Pattern Recognition,
PRL(18), No. 11-13, November 1997, pp. 1391-1398. 9806
BibRef

Murino, V.,
Structured Neural Networks for Pattern Recognition,
SMC-B(28), No. 4, August 1998, pp. 553-561.
IEEE Top Reference. 9808
BibRef

Lu, Z.K., Chi, Z.R., Siu, W.C.,
Length Estimation of Digit Strings Using a Neural Network with Structure Based Features,
JEI(7), No. 1, January 1998, pp. 79-85. 9807
BibRef

Chen, C.W., Chen, L.L.,
Cellular Neural Network Architecture for Gibbs Random Field Based Image Segmentation,
JEI(7), No. 1, January 1998, pp. 45-51. 9807
BibRef

Chen, C.W., Chen, L.L., Luo, J.B.,
A Cellular Neural Network for Clustering-Based Adaptive Quantization in Subband Video Compression,
CirSysVideo(6), No. 6, December 1996, pp. 688-692.
IEEE Top Reference. 9701
BibRef

Aizenberg, I.N.,
Processing of Noisy and Small Detailed Gray Scale Images Using Cellular Neural Networks,
JEI(6), No. 3, July 1997, pp. 272-285. 9807
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Lin, C.C.[Che-Chern], El-Jaroudi, A.[Amro],
An Algorithm to Determine the Feasibilities and Weights of Two-Layer Perceptrons for Partitioning and Classification,
PR(31), No. 11, November 1998, pp. 1613-1625.
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Zhou, W.,
Verification of the Nonparametric Characteristics of Backpropagation Neural Networks for Image Classification,
GeoRS(37), No. 2, March 1999, pp. 771.
IEEE Top Reference. BibRef 9903

Carozza, M.[Menita], Rampone, S.[Salvatore],
Function approximation from noisy data by an incremental RBF network,
PR(32), No. 12, December 1999, pp. 2081-2083.
WWW Link. BibRef 9912
And: Further results:
An incremental multivariate regression method for function approximation from noisy data,
PR(34), No. 3, March 2001, pp. 695-702.
WWW Link. 0101
BibRef

Li, X.L.[Xiao-Lin], Parizeau, M.[Marc], Plamondon, R.[Rejean],
Training Hidden Markov Models with Multiple Observations: A Combinatorial Method,
PAMI(22), No. 4, April 2000, pp. 371-377.
IEEE DOI 0006
BibRef

Lin, C.T., Lee, Y.C., Pu, H.C.,
Satellite Sensor Image Classification Using Cascaded Architecture of Neural Fuzzy Network,
GeoRS(38), No. 2, March 2000, pp. 1033-1043.
IEEE Top Reference. 0004
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Simpson, J.J., McIntire, T.J., Sienko, M.,
An Improved Hybrid Clustering Algorithm for Natural Scenes,
GeoRS(38), No. 2, March 2000, pp. 1016-1032.
IEEE Top Reference. 0004
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Gupta, L.[Lalit], McAvoy, M.[Mark], Phegley, J.[James],
Classification of temporal sequences via prediction using the simple recurrent neural network,
PR(33), No. 10, October 2000, pp. 1759-1770.
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Rughooputh, H.C.S., Rughooputh, S.D.D.V.,
Spectral recognition using a modified Eckhorn neural network model,
IVC(18), No. 14, November 2000, pp. 1101-1103.
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Lin, J.S.[Jzau-Sheng],
Annealed chaotic neural network with nonlinear self-feedback and its application to clustering problem,
PR(34), No. 5, May 2001, pp. 1093-1104.
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Go, J.[Jinwook], Han, G.[Gunhee], Kim, H.[Hagbae], Lee, C.H.[Chul-Hee],
Multigradient: a new neural network learning algorithm for pattern classification,
GeoRS(39), No. 5, May 2001, pp. 986-993.
IEEE Top Reference. 0106
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Iyatomi, H.[Hitoshi], Hagiwara, M.[Masafumi],
Scenery image recognition and interpretation using fuzzy inference neural networks,
PR(35), No. 8, August 2002, pp. 1793-1806.
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Iyatomi, H.[Hitoshi], Hagiwara, M.[Masafumi],
Adaptive fuzzy inference neural network,
PR(37), No. 10, October 2004, pp. 2049-2057.
WWW Link. 0409
Initial rule, selection of important elements, identification of the network structure, parameter estimation. BibRef

Augusteijn, M.F., Folkert, B.A.,
Neural network classification and novelty detection,
JRS(23), No. 14, July 2002, pp. 2891-2902. 0208
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Lin, J.S.[Jzau-Sheng], Liu, S.H.[Shao-Han],
Classification of multispectral images based on a fuzzy-possibilistic neural network,
SMC-C(32), No. 4, November 2002, pp. 499-506.
IEEE Top Reference. 0301
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Jiang, X.D.[Xu-Dong], Wah, A.H.K.S.[Alvin Harvey Kam Siew],
Constructing and training feed-forward neural networks for pattern classification,
PR(36), No. 4, April 2003, pp. 853-867.
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Raudys, A.,
Boosting neural network feature extraction by reduced accuracy activation functions,
PR(36), No. 6, June 2003, pp. 1343-1354.
WWW Link. 0304
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Venkatesh, Y.V., Raja, S.K.[S. Kumar],
On the classification of multispectral satellite images using the multilayer perceptron,
PR(36), No. 9, September 2003, pp. 2161-2175.
WWW Link. 0307
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Porter, R.B.[Reid B.], Harvey, N.R.[Neal R.], Perkins, S.[Simon], Theiler, J.[James], Brumby, S.P.[Steven P.], Bloch, J.J.[Jeffrey J.], Gokhale, M.[Maya], Szymanski, J.J.[John J.],
Optimizing Digital Hardware Perceptrons for Multi-Spectral Image Classification,
JMIV(19), No. 2, September 2003, pp. 133-150.
DOI Link 0308
BibRef

Park, S.B.[Soo Beom], Lee, J.W.[Jae Won], Kim, S.K.[Sang Kyoon],
Content-based image classification using a neural network,
PRL(25), No. 3, February 2004, pp. 287-300.
WWW Link. 0401
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Han, M.[Min], Xi, J.H.[Jian-Hui],
Efficient clustering of radial basis perceptron neural network for pattern recognition,
PR(37), No. 10, October 2004, pp. 2059-2067.
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Alaiz-Rodríguez, R.[Rocío], Guerrero-Curieses, A.[Alicia], Cid-Sueiro, J.[Jesús],
Minimax classifiers based on neural networks,
PR(38), No. 1, January 2005, pp. 29-39.
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Sanz, P.J., Marin, R., Sanchez, J.S.,
Including efficient object recognition capabilities in online robots: from a statistical to a Neural-network classifier,
SMC-C(35), No. 1, February 2005, pp. 87-96.
IEEE Abstract. 0501
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Lim, C.P.[Chee-Peng], Leong, J.H.[Jenn-Hwai], Kuan, M.M.[Mei-Ming],
A Hybrid Neural Network System for Pattern Classification Tasks with Missing Features,
PAMI(27), No. 4, April 2005, pp. 648-653.
IEEE Abstract. 0501
Classification with incomplete data. BibRef

Artyomov, E.[Evgeny], Yadid-Pecht, O.[Orly],
Modified high-order neural network for invariant pattern recognition,
PRL(26), No. 6, 1 May 2005, pp. 843-851.
WWW Link. 0501
BibRef

Yadid-Pecht, O.[Orly], Gur, M.,
A simple 'possibilistic' clustering neural network,
ICPR94(B:520-521).
IEEE DOI 9410
BibRef

Spratling, M.W.[Michael W.],
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images,
PAMI(27), No. 5, May 2005, pp. 753-761.
IEEE Abstract. 0501
Neural network application. Learn simple patterns in all orientations. BibRef

Qin, A.K., Suganthan, P.N.,
Enhanced neural gas network for prototype-based clustering,
PR(38), No. 8, August 2005, pp. 1275-1288.
WWW Link. 0505
Code, Neural Networks. BibRef
Earlier:
Kernel neural gas algorithms with application to cluster analysis,
ICPR04(IV: 617-620).
IEEE DOI 0409
Code available:
WWW Link. BibRef

Delgado, M., Pegalajar, M.C.,
A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference,
PR(38), No. 9, September 2005, 1444-1456.
WWW Link. 0506
BibRef

Chi, H.M.[Hoi-Ming], Ersoy, O.K.,
A statistical self-organizing learning system for remote sensing classification,
GeoRS(43), No. 8, August 2005, pp. 1890-1900.
IEEE DOI 0508
BibRef

Zhang, H., Huang, W., Huang, Z., Zhang, B.,
A Kernel Autoassociator Approach to Pattern Classification,
SMC-B(35), No. 3, June 2005, pp. 593-606.
IEEE DOI 0508
BibRef

Perez, C.A., Gonzalez, G.D., Medina, L.E., Galdames, F.J.,
Linear Versus Nonlinear Neural Modeling for 2-D Pattern Recognition,
SMC-A(35), No. 6, November 2005, pp. 955-964.
IEEE DOI 0510
BibRef

Cang, S., Yu, H.,
Novel probabilty neural network,
VISP(152), No. 5, October 2005, pp. 535-544.
DOI Link 0512
BibRef

Ng, W.W.Y.[Wing W.Y.], Dorado, A.[Andres], Yeung, D.S.[Daniel S.], Pedrycz, W.[Witold], and Izquierdo, E.[Ebroul],
Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error,
PR(40), No. 1, January 2007, pp. 19-32.
WWW Link. 0611
Image classification; Radial basis functions neural networks; MPEG-7; Support vector machines; Generalization error BibRef

Chandramouli, K., Izquierdo, E.,
Image Classification using Chaotic Particle Swarm Optimization,
ICIP06(3001-3004).
IEEE DOI 0610
BibRef

Chung, F.L., Wang, S., Deng, Z., Hu, D.,
CATSMLP: Toward a Robust and Interpretable Multilayer Perceptron With Sigmoid Activation Functions,
SMC-B(36), No. 6, December 2006, pp. 1319-1331.
IEEE DOI 0701
BibRef

del Frate, F.[Fabio], Pacifici, F.[Fabio], Schiavon, G.[Giovanni], Solimini, C.[Chiara],
Use of Neural Networks for Automatic Classification From High-Resolution Images,
GeoRS(45), No. 4, April 2007, pp. 800-809.
IEEE DOI 0704
BibRef

Meher, S.K.[Saroj K.], Uma Shankar, B., Ghosh, A.[Ashish],
Wavelet-Feature-Based Classifiers for Multispectral Remote-Sensing Images,
GeoRS(45), No. 6, June 2007, pp. 1881-1886.
IEEE DOI 0706
BibRef

Shankar, B.U.[B. Uma], Meher, S.K.[Saroj K.], Ghosh, A.[Ashish], Bruzzone, L.[Lorenzo],
Remote Sensing Image Classification: A Neuro-fuzzy MCS Approach,
ICCVGIP06(128-139).
Springer DOI 0612
BibRef

Misra, B.B., Dehuri, S., Dash, P.K., Panda, G.,
A reduced and comprehensible polynomial neural network for classification,
PRL(29), No. 12, 1 September 2008, pp. 1705-1712.
WWW Link. 0804
Classification; Polynomial neural network; Particle swarm optimization BibRef

Ponalagusamy, R., Senthilkumar, S.,
A new fourth order embedded RKAHeM(4,4) method with error control on multilayer raster cellular neural network,
SIViP(3), No. 1, January 2009, pp. xx-yy.
Springer DOI 0902
initial value problems. BibRef

Ponalagusamy, R., Senthilkumar, S.,
A new fourth order embedded RKAHeM(4,4) method with error control on single layer/raster cellular neural network,
SIViP(3), No. 3, September 2009, pp. xx-yy.
Springer DOI 0910
BibRef

Senthilkumar, S.,
Hole-Filler Cellular Neural Network Simulation by RKGHM(5,5),
JMIV(43), No. 3, July 2012, pp. 194-205.
WWW Link. 1204
BibRef

Wang, J.S.[Jeen-Shing], Hsu, Y.L.[Yu-Liang], Lin, H.Y.[Hung-Yi], Chen, Y.P.[Yen-Ping],
Minimal model dimension/order determination algorithms for recurrent neural networks,
PRL(30), No. 9, 1 July 2009, pp. 812-819.
Elsevier DOI 0905
Model dimension/order determination; Nonlinear system identification; Recurrent neural networks; Minimal realization BibRef

Taylor, J.G., Hartley, M., Taylor, N., Panchev, C., Kasderidis, S.,
A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning,
IVC(27), No. 11, 2 October 2009, pp. 1641-1657.
Elsevier DOI 0909
Dorsal and ventral vision; Object representations; Dopamine as reward; TD learning BibRef

Fontenla-Romero, O.[Oscar], Guijarro-Berdinas, B.[Bertha], Perez-Sanchez, B.[Beatriz], Alonso-Betanzos, A.[Amparo],
A new convex objective function for the supervised learning of single-layer neural networks,
PR(43), No. 5, May 2010, pp. 1984-1992.
Elsevier DOI 1003
Single-layer neural networks; Global optimum; Supervised learning method; Least squares; Convex optimization; Incremental learning BibRef

Gross, B.A.[Brooks A.], Hanna, D.M.[Darrin M.],
Artificial neural networks capable of learning spatiotemporal chemical diffusion in the cortical brain,
PR(43), No. 11, November 2010, pp. 3910-3921.
Elsevier DOI 1008
Artificial intelligence; Elman; Neural network; 3D; Chemical imaging; Brain; Neurochemistry BibRef

Yu, D.[Dong], Deng, L.[Li],
Efficient and effective algorithms for training single-hidden-layer neural networks,
PRL(33), No. 5, 1 April 2012, pp. 554-558.
Elsevier DOI 1202
Neural network; Extreme learning machine; Accelerated gradient algorithm; Weighted algorithm; MNIST BibRef

Duin, R.P.W.[Robert P.W.], Pekalska, E.[Elÿzbieta],
The dissimilarity space: Bridging structural and statistical pattern recognition,
PRL(33), No. 7, 1 May 2012, pp. 826-832.
Elsevier DOI 1203
Award, King Sun Fu. BibRef
Earlier:
On refining dissimilarity matrices for an improved NN learning,
ICPR08(1-4).
IEEE DOI 0812
Dissimilarity representation; Representation set; Dissimilarity space; Vector space; Structural pattern recognition BibRef

Martínez-Rego, D.[David], Fontenla-Romero, O.[Oscar], Alonso-Betanzos, A.[Amparo],
Nonlinear single layer neural network training algorithm for incremental, nonstationary and distributed learning scenarios,
PR(45), No. 12, December 2012, pp. 4536-4546.
Elsevier DOI 1208
Artificial neural networks; Incremental learning; Nonstationary learning; Distributed learning BibRef

Martinez-Rego, D.[David], Castillo, E.[Enrique], Fontenla-Romero, O.[Oscar], Alonso-Betanzos, A.[Amparo],
A Minimum Volume Covering Approach with a Set of Ellipsoids,
PAMI(35), No. 12, 2013, pp. 2997-3009.
IEEE DOI 1311
Classification BibRef

Benalcázar, M.[Marco], Brun, M.[Marcel], Ballarin, V.[Virginia], Passoni, I.[Isabel], Meschino, G.[Gustavo], Pra, L.D.[Lucía Dai],
Automatic Design of Binary W-operators Using Artificial Feed-forward Neural Networks Based on the Weighted Mean Square Error Cost Function,
CIARP12(495-502).
Springer DOI 1209
BibRef

Chen, B.[Bo], Polatkan, G.[Gungor], Sapiro, G.[Guillermo], Blei, D.[David], Dunson, D.[David], Carin, L.[Lawrence],
Deep Learning with Hierarchical Convolutional Factor Analysis,
PAMI(35), No. 8, 2013, pp. 1887-1901.
IEEE DOI 1307
Analytical models; Bayesian methods; Convolution; deep learning BibRef

Goodfellow, I.J.[Ian J.], Courville, A.[Aaron], Bengio, Y.[Yoshua],
Scaling Up Spike-and-Slab Models for Unsupervised Feature Learning,
PAMI(35), No. 8, 2013, pp. 1902-1914.
IEEE DOI 1307
Approximation methods; Neural nets BibRef

Courville, A.[Aaron], Desjardins, G., Bergstra, J., Bengio, Y.[Yoshua],
The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions,
PAMI(36), No. 9, September 2014, pp. 1874-1887.
IEEE DOI 1408
Covariance matrices BibRef

Li, D., Wang, W., Ismail, F.,
Fuzzy Neural Network Technique for System State Forecasting,
Cyber(43), No. 5, 2013, pp. 1484-1494.
IEEE DOI 1309
Fuzzy neural predictors BibRef

Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Multi-Class Multi-Scale Series Contextual Model for Image Segmentation,
IP(22), No. 11, 2013, pp. 4486-4496.
IEEE DOI 1310
electron microscopy BibRef

Seyedhosseini, M.[Mojtaba], Paiva, A.R.C.[Antonio R.C.], Tasdizen, T.[Tolga],
Image Parsing with a Three-State Series Neural Network Classifier,
ICPR10(4508-4511).
IEEE DOI 1008
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Singh, A.[Abhishek], Pokharel, R.[Rosha], Principe, J.C.[Jose C.],
The C-loss function for pattern classification,
PR(47), No. 1, 2014, pp. 441-453.
Elsevier DOI 1310
Correntropy. For neural network classification. BibRef

Chen, C.H.[Ching-Han], Kuo, C.M.[Chia-Ming], Yao, T.K.[Tun-Kai], Hsieh, S.H.[Sheng-Hsien],
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JMIV(48), No. 3, March 2014, pp. 488-498.
Springer DOI 1403
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Zuo, Z., Wang, G.,
Learning Discriminative Hierarchical Features for Object Recognition,
SPLetters(21), No. 9, Sept 2014, pp. 1159-1163.
IEEE DOI 1406
Artificial neural networks BibRef

Alvar, M.[Manuel], Rodriguez-Calvo, A.[Andrea], Sanchez-Miralles, A.[Alvaro], Arranz, A.[Alvaro],
Mixture of Merged Gaussian Algorithm using RTDENN,
MVA(25), No. 5, July 2014, pp. 1133-1144.
Springer DOI 1407
RTDENN: Real-Time Dynamic Ellipsoidal Neural Networks BibRef

Chatzis, S.P.[Sotirios P.], Demiris, Y.[Yiannis],
The copula echo state network,
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Elsevier DOI 1410
Copula Echo state networks for Recurrent NN training. BibRef

Charalampous, K.[Konstantinos], Gasteratos, A.[Antonios],
A tensor-based deep learning framework,
IVC(32), No. 11, 2014, pp. 916-929.
Elsevier DOI 1410
Deep learning BibRef

Ramirez-Quintana, J.A.[Juan Alberto], Chacon-Murguia, M.I.[Mario Ignacio],
Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios,
PR(48), No. 4, 2015, pp. 1137-1149.
Elsevier DOI 1502
Video analysis BibRef

Luo, W.[Wei], Yang, J.[Jian], Xu, W.[Wei], Fu, T.[Tao],
Locality-Constrained Sparse Auto-Encoder for Image Classification,
SPLetters(22), No. 8, August 2015, pp. 1070-1073.
IEEE DOI 1502
image classification BibRef

Harikumar, R., kumar, B.V.[B. Vinoth],
Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor,
IJIST(25), No. 1, 2015, pp. 33-40.
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Iakymchuk, T.[Taras], Rosado-Munoz, A.[Alfredo], Guerrero-Martinez, J.[Juan], Bataller-Mompean, M.[Manuel], Frances-Villora, J.[Jose],
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JIVP(2015), No. 1, 2015, pp. 4.
DOI Link 1503
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Liu, J.[Jing], Liu, B.Y.[Bing-Yuan], Lu, H.Q.[Han-Qing],
Detection guided deconvolutional network for hierarchical feature learning,
PR(48), No. 8, 2015, pp. 2645-2655.
Elsevier DOI 1505
Image representation BibRef

Choi, J.S.[Jae Seung],
Discrimination algorithm using voiced detection method and time-delay neural network system by 3 FFT sub-bands,
IJCVR(5), No. 2, 2015, pp. 99-111.
DOI Link 1505
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Kong, S., Jiang, Z., Yang, Q.,
Modeling Neuron Selectivity Over Simple Midlevel Features for Image Classification,
IP(24), No. 8, August 2015, pp. 2404-2414.
IEEE DOI 1505
Convolution BibRef

Lerouge, J., Herault, R., Chatelain, C., Jardin, F., Modzelewski, R.,
IODA: An input/output deep architecture for image labeling,
PR(48), No. 9, 2015, pp. 2847-2858.
Elsevier DOI 1506
Deep learning architectures BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, G.[Gang],
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling,
SPLetters(22), No. 11, November 2015, pp. 1990-1994.
IEEE DOI 1509
feature extraction See also Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification. BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, B.[Bing], Wang, G.[Gang],
DAG-Recurrent Neural Networks for Scene Labeling,
CVPR16(3620-3629)
IEEE DOI 1612
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Manju, S., Punithavalli, M.,
Neural network-based ideation learning for intelligent agents: e-brainstorming with privacy preferences,
IJCVR(5), No. 3, 2015, pp. 231-253.
DOI Link 1509
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Zhu, S.H.[Song-Hao], Shi, Z.[Zhe], Sun, C.J.[Cheng-Jian], Shen, S.[Shuhan],
Deep neural network based image annotation,
PRL(65), No. 1, 2015, pp. 103-108.
Elsevier DOI 1511
Deep learning BibRef
Earlier: A3, A1, A2, Only:
Image annotation via deep neural network,
MVA15(518-521)
IEEE DOI 1507
Computer architecture BibRef

Huang, Y.[Yan], Wang, W.[Wei], Wang, L.[Liang],
Unconstrained Multimodal Multi-Label Learning,
MultMed(17), No. 11, November 2015, pp. 1923-1935.
IEEE DOI 1511
BibRef
Earlier:
Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning,
ICCV15(4265-4273)
IEEE DOI 1602
Correlation BibRef

Huang, Y.[Yan], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Conditional High-Order Boltzmann Machines for Supervised Relation Learning,
IP(26), No. 9, September 2017, pp. 4297-4310.
IEEE DOI 1708
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Earlier:
Multi-Task Deep Neural Network for Multi-Label Learning,
ICIP13(2897-2900)
IEEE DOI 1402
Boltzmann machines, face recognition, image classification, learning (artificial intelligence), matrix decomposition, tensors, CHBM, action similarity labeling, binary classification, conditional high-order Boltzmann machines, conditional likelihood, data relation, discriminant ability enhancement, face verification, high-order multiplicative interactions, high-order parameter tensors, invariant recognition, joint likelihood, latent variables, multiple matrix factorization, pairwise input samples, relation feature classification, relation feature learning, supervised relation learning, Computational modeling, Data models, Face, Logic gates, Measurement, Supervised learning, Tensile stress, Deep learning, action similarity labeling, face verification, high-order Boltzmann machine, relation learning BibRef

An, H.[Hongsub], Shim, H.M.[Hyeon-Min], Na, S.I.[Sang-Il], Lee, S.[Sangmin],
Split and merge algorithm for deep learning and its application for additional classes,
PRL(65), No. 1, 2015, pp. 137-144.
Elsevier DOI 1511
Deep neural networks BibRef

Hile, R.[Ryan], Cova, T.J.[Thomas J.],
Exploratory Testing of an Artificial Neural Network Classification for Enhancement of the Social Vulnerability Index,
IJGI(4), No. 4, 2015, pp. 1774.
DOI Link 1511
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Ye, Y.B.[Yi-Bin], Qin, Y.[Yang],
QR factorization based Incremental Extreme Learning Machine with growth of hidden nodes,
PRL(65), No. 1, 2015, pp. 177-183.
Elsevier DOI 1511
Extreme Learning Machine(ELM) BibRef

Sengoz, C.[Cenker], Ramanna, S.[Sheela],
Learning relational facts from the web: A tolerance rough set approach,
PRL(67, Part 2), No. 1, 2015, pp. 130-137.
Elsevier DOI 1512
Tolerance rough sets BibRef

Xu, X.Z.[Xin-Zheng], Wang, G.Y.[Guan-Ying], Ding, S.F.[Shi-Fei], Jiang, X.Y.[Xiang-Ying], Zhao, Z.P.[Zuo-Peng],
A new method for constructing granular neural networks based on rule extraction and extreme learning machine,
PRL(67, Part 2), No. 1, 2015, pp. 138-144.
Elsevier DOI 1512
Granular neural networks BibRef

Yang, Y.[Yang], Zhang, W.S.[Wen-Sheng], Xie, Y.[Yuan],
Image automatic annotation via multi-view deep representation,
JVCIR(33), No. 1, 2015, pp. 368-377.
Elsevier DOI 1512
Image annotation BibRef

Burian, P.[Petr], Holota, R.[Radek],
Fast image recognition based on n-tuple neural networks implemented in an FPGA,
RealTimeIP(11), No. 1, January 2016, pp. 155-166.
WWW Link. 1601
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Azizpour, H.[Hossein], Razavian, A.S.[Ali Sharif], Sullivan, J.[Josephine], Maki, A.[Atsuto], Carlsson, S.[Stefan],
Factors of Transferability for a Generic ConvNet Representation,
PAMI(38), No. 9, September 2016, pp. 1790-1802.
IEEE DOI 1609
BibRef
Earlier:
From generic to specific deep representations for visual recognition,
DeepLearn15(36-45)
IEEE DOI 1510
feature extraction BibRef

Razavian, A.S.[Ali Sharif], Azizpour, H.[Hossein], Maki, A.[Atsuto], Sullivan, J.[Josephine], Ek, C.H.[Carl Henrik], Carlsson, S.[Stefan],
Persistent Evidence of Local Image Properties in Generic ConvNets,
SCIA15(249-262).
Springer DOI 1506
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Dash, C.S.K.[C. Sanjeev Kumar], Saran, A.[Amitav], Sahoo, P.[Pulak], Dehuri, S.[Satchidananda], Cho, S.B.[Sung-Bae],
Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database,
PRL(80), No. 1, 2016, pp. 76-83.
Elsevier DOI 1609
Data mining BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Neural Bag-of-Features learning,
PR(64), No. 1, 2017, pp. 277-294.
Elsevier DOI 1701
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And:
Bag of Embedded Words learning for text retrieval,
ICPR16(2416-2421)
IEEE DOI 1705
Encoding, Entropy, Feature extraction, Histograms, Quantization (signal), Semantics, Training. Bag-of-Features BibRef

Passalis, N.[Nikolaos], Tefas, A.[Anastasios],
Learning Neural Bag-of-Features for Large-Scale Image Retrieval,
SMCS(47), No. 10, October 2017, pp. 2641-2652.
IEEE DOI 1709
Dictionaries, Encoding, Feature extraction, Histograms, Image retrieval, Image segmentation, Bag-of-features (BoFs) representation, information retrieval, neural networks, retrieval-oriented, optimization BibRef

Ahsan, A.M.[Amin Mohamed], Mohamad, D.B.[Dzulkifli Bin],
Machine learning technique for object detection based on SURF feature,
IJCVR(7), No. 1/2, 2017, pp. 6-19.
DOI Link 1701
NN learning using SURF features BibRef

Saleh, A.Y.[Abdulrazak Yahya], Shamsuddin, S.M.[Siti Mariyam], Hamed, H.N.A.[Haza Nuzly Abdull],
A hybrid differential evolution algorithm for parameter tuning of evolving spiking neural network,
IJCVR(7), No. 1/2, 2017, pp. 20-34.
DOI Link 1701
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Bakhtiary, A.H.[Amir H.], Lapedriza, A.[Agata], Masip, D.[David],
Winner takes all hashing for speeding up the training of neural networks in large class problems,
PRL(93), No. 1, 2017, pp. 38-47.
Elsevier DOI 1706
Winner takes all hashing BibRef

Liao, Z.B.[Zhi-Bin], Carneiro, G.[Gustavo],
A deep convolutional neural network module that promotes competition of multiple-size filters,
PR(71), No. 1, 2017, pp. 94-105.
Elsevier DOI 1707
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Earlier:
The use of deep learning features in a hierarchical classifier learned with the minimization of a non-greedy loss function that delays gratification,
ICIP15(4540-4544)
IEEE DOI 1512
Deep, learning BibRef


Kaoutar, S., Mohamed, E.,
Multi-criteria optimization of neural networks using multi-objective genetic algorithm,
ISCV17(1-4)
IEEE DOI 1710
Pareto optimisation, genetic algorithms, minimisation, multilayer perceptrons, vectors, MLPNN, NSGA II algorithm, Pareto set, absolute weights, architecture objective optimization, BibRef

Srinivas, S., Subramanya, A., Babu, R.V.,
Training Sparse Neural Networks,
ECVW17(455-462)
IEEE DOI 1709
Biological neural networks, Complexity theory, Indexes, Logic gates, Sparse matrices, Training BibRef

Meng, N., So, H.K.H., Lam, E.Y.,
Computational single-cell classification using deep learning on bright-field and phase images,
MVA17(190-193)
DOI Link 1708
Computer architecture, Feature extraction, Imaging, Machine learning, Microprocessors, Neural networks, Training BibRef

Cui, S.[Shuqi], Jiang, H.[Hong], Wang, Z.[Zheng], Shen, C.M.[Chao-Min],
Application of neural network based on SIFT local feature extraction in medical image classification,
ICIVC17(92-97)
IEEE DOI 1708
Biological neural networks, Feature extraction, Image classification, Medical diagnostic imaging, Neurons, BP neural network, ROI, SIFT, SVM, slide, the, window BibRef

Kampffmeyer, M.[Michael], Løkse, S.[Sigurd], Bianchi, F.M.[Filippo M.], Jenssen, R.[Robert], Livi, L.[Lorenzo],
Deep Kernelized Autoencoders,
SCIA17(I: 419-430).
Springer DOI 1706
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Nilsson, N., Ortiz-Catalan, M.,
Estimates of Classification Complexity for Myoelectric Pattern Recognition,
ICPR16(2682-2687)
IEEE DOI 1705
Artificial neural networks, Complexity theory, Correlation, Electromyography, Indexes, Pattern recognition, Silicon BibRef

Peng, K.H.[Kang-Hao], Zhang, H.[Heng],
Mutual information-based RBM neural networks,
ICPR16(2458-2463)
IEEE DOI 1705
Annealing, Entropy, Manganese, Monte Carlo methods, Mutual information, Neural networks, Training BibRef

Hasegawa, R.[Ryoma], Hotta, K.[Kazuhiro],
PLSNet: A simple network using Partial Least Squares regression for image classification,
ICPR16(1601-1606)
IEEE DOI 1705
Convolution, Databases, Feature extraction, Image classification, Network architecture, Principal component analysis, Training, Convolutional Neural Network, Deep Learning, PCANet, PLSNet, Partial Least Squares Regression, Stacked, PLS BibRef

Liu, L.[Lei],
Hierarchical learning for large multi-class network classification,
ICPR16(2307-2312)
IEEE DOI 1705
Additives, Computational modeling, Covariance matrices, Linear programming, Matrix decomposition, Optimization, Testing BibRef

Kalra, S., Sriram, A., Rahnamayan, S., Tizhoosh, H.R.,
Learning opposites using neural networks,
ICPR16(1213-1218)
IEEE DOI 1705
Approximation algorithms, Convergence, Data mining, Neural networks, Optimization, Training, Training, data BibRef

Wang, Q., Li, P.,
D-LSM: Deep Liquid State Machine with unsupervised recurrent reservoir tuning,
ICPR16(2652-2657)
IEEE DOI 1705
Biological neural networks, Convolution, Feature extraction, Kernel, Liquids, Neurons, Reservoirs BibRef

Roy, A., Todorovic, S., Latecki, L.J.,
Context-regularized learning of fully convolutional networks for scene labeling,
ICPR16(3751-3756)
IEEE DOI 1705
Context, Labeling, Layout, Semantics, Standards, Training, Training, data BibRef

Nooka, S.P., Chennupati, S., Veerabhadra, K., Sah, S., Ptucha, R.,
Adaptive hierarchical classification networks,
ICPR16(3578-3583)
IEEE DOI 1705
Adaptation models, Adaptive systems, Computer architecture, Couplings, Feature extraction, Neural networks, Training, Convolutional Neural Network, Decomposition, Hierarchy, Image Classification, Muli-layer, Perceptron BibRef

Williams, P.[Phillip],
SINN: Shepard Interpolation Neural Networks,
ISVC16(II: 349-358).
Springer DOI 1701
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Rajagopalan, S.S.[Shyam Sundar], Morency, L.P.[Louis-Philippe], Baltrusaitis, T.[Tadas], Goecke, R.[Roland],
Extending Long Short-Term Memory for Multi-View Structured Learning,
ECCV16(VII: 338-353).
Springer DOI 1611
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Zhang, J.M.[Jian-Ming], Lin, Z.[Zhe], Brandt, J.[Jonathan], Shen, X.H.[Xiao-Hui], Sclaroff, S.[Stan],
Top-Down Neural Attention by Excitation Backprop,
ECCV16(IV: 543-559).
Springer DOI 1611
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Liu, S.[Sifei], Pan, J.[Jinshan], Yang, M.H.[Ming-Hsuan],
Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network,
ECCV16(IV: 560-576).
Springer DOI 1611
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Srinivas, S.[Suraj], Babu, R.V.[R. Venkatesh],
Data-free Parameter Pruning for Deep Neural Networks,
BMVC15(xx-yy).
DOI Link 1601
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Huang, Y.C.[Yu-Chi], Sun, X.Y.[Xiu-Yu], Lu, M.[Ming], Xu, M.[Ming],
Channel-Max, Channel-Drop and Stochastic Max-pooling,
DeepLearn15(9-17)
IEEE DOI 1510
Color BibRef

Lin, K.[Kevin], Yang, H.F.[Huei-Fang], Hsiao, J.H.[Jen-Hao], Chen, C.S.[Chu-Song],
Deep learning of binary hash codes for fast image retrieval,
DeepLearn15(27-35)
IEEE DOI 1510
Binary codes BibRef

Oyallon, E.[Edouard], Mallat, S.[Stephane],
Deep roto-translation scattering for object classification,
CVPR15(2865-2873)
IEEE DOI 1510
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Lai, H.J.[Han-Jiang], Pan, Y.[Yan], Liu, Y.[Ye], Yan, S.C.[Shui-Cheng],
Simultaneous feature learning and hash coding with deep neural networks,
CVPR15(3270-3278)
IEEE DOI 1510
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Shankar, S.[Sukrit], Garg, V.K.[Vikas K.], Cipolla, R.[Roberto],
DEEP-CARVING: Discovering visual attributes by carving deep neural nets,
CVPR15(3403-3412)
IEEE DOI 1510
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Perronnin, F.[Florent], Larlus, D.[Diane],
Fisher vectors meet Neural Networks: A hybrid classification architecture,
CVPR15(3743-3752)
IEEE DOI 1510
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Verbancsics, P.[Phillip], Harguess, J.[Josh],
Image Classification Using Generative Neuro Evolution for Deep Learning,
WACV15(488-493)
IEEE DOI 1503
Accuracy BibRef

Aviles, A.I., Marban, A., Sobrevilla, P., Fernandez, J., Casals, A.,
A recurrent neural network approach for 3D vision-based force estimation,
IPTA14(1-6)
IEEE DOI 1503
dexterous manipulators BibRef

Hillar, C.[Christopher], Mehta, R.[Ram], Koepsell, K.[Kilian],
A hopfield recurrent neural network trained on natural images performs state-of-the-art image compression,
ICIP14(4092-4096)
IEEE DOI 1502
Image coding BibRef

Tang, J.X.[Jie-Xiong], Deng, C.W.[Chen-Wei], Huang, G.B.[Guang-Bin], Hou, J.H.[Jun-Hui],
A fast learning algorithm for multi-layer extreme learning machine,
ICIP14(175-178)
IEEE DOI 1502
Accuracy BibRef

Li, W.B.[Wen-Bin],
Learning Multi-scale Representations for Material Classification,
GCPR14(757-764).
Springer DOI 1411
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Mendoza-Castañeda, E.[Efraín], Reyes-García, C.A.[Carlos A.], Escalante, H.J.[Hugo Jair], Moreno, W.[Wilfrido], Rosales-Pérez, A.[Alejandro],
Enhanced Fuzzy-Relational Neural Network with Alternative Relational Products,
CIARP14(666-673).
Springer DOI 1411
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Ocampo-Vega, R.[Ricardo], Sanchez-Ante, G.[Gildardo], Falcon-Morales, L.E.[Luis E.], Sossa, H.[Humberto],
Automatic Construction of Radial-Basis Function Networks Through an Adaptive Partition Algorithm,
MCPR16(198-207).
Springer DOI 1608
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Sossa, H.[Humberto], Cortés, G.[Griselda], Guevara, E.[Elizabeth],
New Radial Basis Function Neural Network Architecture for Pattern Classification: First Results,
CIARP14(706-713).
Springer DOI 1411
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Espinal, A.[Andrés], Carpio, M.[Martín], Ornelas, M.[Manuel], Puga, H.[Héctor], Melín, P.[Patricia], Sotelo-Figueroa, M.[Marco],
Developing Architectures of Spiking Neural Networks by Using Grammatical Evolution Based on Evolutionary Strategy,
MCPR14(71-80).
Springer DOI 1407
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Shashi Kumar, M.S., Vimala, K.S., Avinash, N.,
Face distance estimation from a monocular camera,
ICIP13(3532-3536)
IEEE DOI 1402
Back propagation neural network BibRef

Landassuri-Moreno, V.M.[Víctor Manuel], Bustillo-Hernández, C.L.[Carmen L.],
Single-Step-Ahead and Multi-Step-Ahead Prediction with Evolutionary Artificial Neural Networks,
CIARP13(I:65-72).
Springer DOI 1311
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Orjuela-Cañón, A.D.[Alvaro D.], Delisle-Rodríguez, D.[Denis], López-Delis, A.[Alberto],
Onset and Peak Pattern Recognition on Photoplethysmographic Signals Using Neural Networks,
CIARP13(I:543-550).
Springer DOI 1311
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Chien, C.J.[Chiang-Ju], Wang, Y.C.[Ying-Chung],
Observer based adaptive control of nonlinear systems using filtered-FNN design,
ICARCV12(52-57).
IEEE DOI 1304
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An FNN-Based adaptive iterative learning control for a class of nonlinear discrete-time systems,
ICARCV12(447-451).
IEEE DOI 1304
Fuzzy Neural Network BibRef

Zhuo, W.[Wen], Cao, Z.G.[Zhi-Guo], Qin, Y.M.[Yue-Ming], Yu, Z.H.[Zheng-Hong], Xiao, Y.[Yang],
Image classification using HTM cortical learning algorithms,
ICPR12(2452-2455).
WWW Link. 1302
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Sossa, H.[Humberto], Garro, B.A.[Beatriz A.], Villegas, J.[Juan], Avilés, C.[Carlos], Olague, G.[Gustavo],
Automatic Design of Artificial Neural Networks and Associative Memories for Pattern Classification and Pattern Restoration,
MCPR12(23-34).
Springer DOI 1208
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Varvak, M.S.[Mark S.],
Pattern Classification Using Radial Basis Function Neural Networks Enhanced with the Rvachev Function Method,
CIARP11(272-279).
Springer DOI 1111
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Nussbaum-Thom, M.[Markus], Schweiger, R.[Roland], Palm, G.[Günther],
Training of Sparsely Connected MLPs,
DAGM11(356-365).
Springer DOI 1109
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Vajda, S.[Szilard], Fink, G.A.[Gernot A.],
Strategies for Training Robust Neural Network Based Digit Recognizers on Unbalanced Data Sets,
FHR10(148-153).
IEEE DOI 1011
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Exploring Pattern Selection Strategies for Fast Neural Network Training,
ICPR10(2913-2916).
IEEE DOI 1008
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Adhyaru, D.M.[Dipak M.], Kar, I.N., Gopal, M.,
Constrained Control of Weakly Coupled Nonlinear Systems Using Neural Network,
PReMI09(567-572).
Springer DOI 0912
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Huo, P.[Peng], Shiu, S.C.K.[Simon Chi-Keung], Wang, H.B.[Hai-Bo], Niu, B.[Ben],
Case Indexing Using PSO and ANN in Real Time Strategy Games,
PReMI09(106-115).
Springer DOI 0912
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Dhumal, A.[Abhishek], Narayanan, R.G.[R. Ganesh], Kumar, G.S.[G. Saravana],
Estimation of Tailor-Welded Blank Parameters for Acceptable Tensile Behaviour Using ANN,
PReMI09(140-145).
Springer DOI 0912
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Jang, H.H.[Hong-Hoon], Park, A.[Anjin], Jung, K.C.[Kee-Chul],
Neural Network Implementation Using CUDA and OpenMP,
DICTA08(155-161).
IEEE DOI 0812
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Rubi-Velez, A.[Anna], Gomez-Ramirez, E.[Eduardo], Pazienza, G.E.[Giovanni E.],
Computing the Weights of Polynomial Cellular Neural Networks Using Quadratic Programming,
CIARP09(645-652).
Springer DOI 0911
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Stasiak, B.[Bartlomiej],
Two-Dimensional Fast Orthogonal Neural Network for Image Recognition,
CIARP09(653-660).
Springer DOI 0911
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Chen, F.Y.[Fang-Yue], Chen, L.[Lin], Jin, W.F.[Wei-Feng],
Robust Designs of Selected Objects Extraction CNN,
CISP09(1-3).
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cellular neural/nonlinear network. BibRef

Liu, W.[Wei], Li, W.H.[Wen-Hui],
An Algorithmic Framework to the Optimal Mapping Function by a Radial Basis Function Neural Network,
CISP09(1-4).
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Wang, L.[Lei], Wen, X.B.[Xian-Bin], Jiao, X.[Xu], Zhang, J.G.[Jian-Guang],
Object Recognition Using a Bayesian Network Imitating Human Neocortex,
CISP09(1-5).
IEEE DOI 0910
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Peerasathein, T., Woo, M.[Myung], Gaborski, R.S.,
Biologically Inspired Object Categorization in Cluttered Scenes,
AIPR07(117-122).
IEEE DOI 0710
I.e. recognize what separately from where. Implement the what is it, not where is it. BibRef

Sporns, O.,
Complex neural networks as future tools in imagery analysis,
AIPR04(67-72).
IEEE DOI 0410
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Flynn, M., Abarbanel, H., Kenyon, G.,
Neurally-based algorithms for image processing,
AIPR04(79-85).
IEEE DOI 0410
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Firpi, H.A., Goodman, E.,
Swarmed feature selection,
AIPR04(112-118).
IEEE DOI 0410
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Firpi, H.A., Goodman, E.D.,
Designing templates for cellular neural networks using particle swarm optimization,
AIPR04(119-123).
IEEE DOI 0410
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Ebner, M.[Marc],
Engineering of Computer Vision Algorithms Using Evolutionary Algorithms,
ACIVS09(367-378).
Springer DOI 0909
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Xiao, P.[Ping], Shi, Y.X.[Yue-Xiang], Xie, W.L.[Wen-Lan],
A novel method of mapping semantic gap to classify natural images,
IASP09(166-171).
IEEE DOI 0904
gap between low level processing and high level recognition. Color and texture, then Neural Network to map features. BibRef

Scripps, J.[Jerry], Tan, P.N.[Pang-Ning], Chen, F.L.[Fei-Long], Esfahanian, A.H.[Abdol-Hossein],
A matrix alignment approach for link prediction,
ICPR08(1-4).
IEEE DOI 0812
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Adeodato, P.J.L., Vasconcelos, G.C., Arnaud, A.L., Cunha, R.C.L.V., Monteiro, D.S.M.P.,
A systematic solution for the NN3 Forecasting Competition problem based on an ensemble of MLP neural networks,
ICPR08(1-4).
IEEE DOI 0812
Multilayer Perceptron BibRef

Kiranyaz, S.[Serkan], Ince, T.[Turker], Yildirim, A.[Alper], Gabbouj, M.[Moncef],
Unsupervised design of Artificial Neural Networks via multi-dimensional Particle Swarm Optimization,
ICPR08(1-4).
IEEE DOI 0812
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Barrón, R.[Ricardo], Sossa, H.[Humberto], Cruz, B.[Benjamín],
A New Algorithm for Training Multi-layered Morphological Networks,
CIARP07(546-555).
Springer DOI 0711
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García, A.[Antonio], León, C.[Carlos], Monedero, I.[Iñigo], Ropero, J.[Jorge],
A Precise Electrical Disturbance Generator for Neural Network Training with Real Level Output,
CIARP07(534-545).
Springer DOI 0711
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Canales, F.[Fernando], Chacón, M.[Max],
Modification of the Growing Neural Gas Algorithm for Cluster Analysis,
CIARP07(684-693).
Springer DOI 0711
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Siebel, N.T.[Nils T.], Krause, J.[Jochen], Sommer, G.[Gerald],
Efficient Learning of Neural Networks with Evolutionary Algorithms,
DAGM07(466-475).
Springer DOI 0709
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Stanojevic, M.[Mladen], Vraneš, S.[Sanja],
Applying Neural Networks to Knowledge Representation and Determination of Its Meaning,
BVAI07(523-532).
Springer DOI 0710
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di Garbo, A.[Angelo], Barbi, M.[Michele], Chillemi, S.[Santi],
Coincidence Detector Properties of Small Networks of Interneurons,
BVAI07(408-417).
Springer DOI 0710
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Domijan, D.[Dražen], Šetic, M.[Mia],
Computing the Maximum Using Presynaptic Inhibition with Glutamate Receptors,
BVAI07(418-427).
Springer DOI 0710
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Pazienti, A.[Antonio], Diesmann, M.[Markus], Grün, S.[Sonja],
Bounds of the Ability to Destroy Precise Coincidences by Spike Dithering,
BVAI07(428-437).
Springer DOI 0710
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Oberhoff, D.[Daniel], Kolesnik, M.[Marina],
Neural Object Recognition by Hierarchical Learning and Extraction of Essential Shapes,
BVAI07(288-297).
Springer DOI 0710
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Kumar, N.[Niraj], Agrawal, A.[Anupam],
Nonparametric Neural Network Model Based on Rough-Fuzzy Membership Function for Classification of Remotely Sensed Images,
ICCVGIP06(106-117).
Springer DOI 0612
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Wysoski, S.G.[Simei Gomes], Benuskova, L.[Lubica], Kasabov, N.[Nikola],
Adaptive Learning Procedure for a Network of Spiking Neurons and Visual Pattern Recognition,
ACIVS06(1133-1142).
Springer DOI 0609
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Nandedkar, A.V., Biswas, P.K.,
Object Recognition Using Reflex Fuzzy Min-Max Neural Network with Floating Neurons,
ICCVGIP06(597-609).
Springer DOI 0612
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Earlier:
A Reflex Fuzzy Min Max Neural Network for Granular Data Classification,
ICPR06(II: 650-653).
IEEE DOI 0609
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Earlier:
A fuzzy min-max neural network classifier with compensatory neuron architecture,
ICPR04(IV: 553-556).
IEEE DOI 0409
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Bianchini, M.[Monica], Maggini, M.[Marco], Sarti, L.[Lorenzo],
Object Localization Using Input/Output Recursive Neural Networks,
ICPR06(III: 95-98).
IEEE DOI 0609
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And:
Object Recognition Using Multiresolution Trees,
SSPR06(331-339).
Springer DOI 0608
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Nieuwenhuis, C.[Claudia], Yan, M.[Michelle],
Knowledge Based Image Enhancement Using Neural Networks,
ICPR06(III: 814-817).
IEEE DOI 0609
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Zhang, Q.A.[Qi-Ang], Liu, W.B.[Wen-Bing], Wei, X.P.[Xiao-Peng], Xu, J.[Jin],
Globally Exponential Stability of Non-autonomous Delayed Neural Networks,
IbPRIA05(II:91).
Springer DOI 0509
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Kuzmenko, A., Zagoruyko, N.,
Structure relaxation method for self-organizing neural networks,
ICPR04(IV: 589-592).
IEEE DOI 0409
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Chen, J.M.[Jin-Miao], Chaudhari, N.S.,
Improvement of bidirectional recurrent neural network for learning long-term dependencies,
ICPR04(IV: 593-596).
IEEE DOI 0409
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Ou, G.B.[Guo-Bin], Murphey, Y.L.[Yi Lu],
Multi-class pattern classification using neural networks,
PR(40), No. 1, January 2007, pp. 4-18.
WWW Link. 0611
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Earlier: Add A3: Feldkamp, L.[Lee], ICPR04(IV: 585-588).
IEEE DOI 0409
Machine learning; Multi-class classification; Neural networks BibRef

Murphey, Y.L.[Yi Lu], Luo, Y.[Yun],
Feature extraction for a multiple pattern classification neural network system,
ICPR02(II: 220-223).
IEEE DOI 0211
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Kell, M.S., Cristobal, G., Neumann, H.,
Neural mechanisms for segregation and recovering of intrinsic images features,
ICIP03(I: 693-696).
IEEE DOI 0312
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Wehrmann, F.[Felix], Bengtsson, E.[Ewert],
Modelling Non-linearities in Images Using an Auto-associative Neural Network,
CAIP03(754-761).
Springer DOI 0311
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Perwass, C.[Christian], Banarer, V.[Vladimir], Sommer, G.[Gerald],
Spherical Decision Surfaces Using Conformal Modelling,
DAGM03(9-16).
Springer DOI 0310
Hypersphere neuron. BibRef

Thorpe, S.[Simon],
Ultra-Rapid Scene Categorization with a Wave of Spikes,
BMCV02(1 ff.).
Springer DOI 0303
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Huang, Y.S.[Yea-Shuan], Tsai, Y.H.[Yao-Hong],
An RBF-based pattern recognition method by competitively reducing classification-oriented error,
ICPR02(II: 180-183).
IEEE DOI 0211
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Toh, K.A.[Kar-Ann], Lu, J.W.[Ju-Wei], Yau, W.Y.[Wei-Yun],
Global Feedforward Neural Network Learning for Classification and Regression,
EMMCVPR01(407-422).
Springer DOI 0205
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Gentili, S.,
Information Update on Neural Tree Networks,
ICIP01(I: 505-508).
IEEE DOI 0108
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Raudys, S.J.,
Prior Weights in Adaptive Pattern Classification,
ICPR00(Vol II: 1010-1013).
IEEE DOI 0009
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Aizenberg, I., Aizenberg, N., Butakov, C., Farberov, E.,
Image Recognition on the Neural Network Based on Multi-valued Neurons,
ICPR00(Vol II: 989-992).
IEEE DOI 0009
Faces. BibRef

Fyfe, C., Lai, P.L.,
Canonical Correlation Analysis Neural Networks,
ICPR00(Vol II: 977-980).
IEEE DOI 0009
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Messer, K., Kittler, J.V.,
Fast Unit Selection Algorithm for Neural Network Design,
ICPR00(Vol II: 981-984).
IEEE DOI 0009
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de Sousa, R., de Carvalho, J.M., de Assis, F.,
Designing Translation Invariant Operations Via Neural Network Training,
ICIP00(Vol I: 908-911).
IEEE DOI 0008
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Heidemann, G., Lücke, D., Ritter, H.,
A System for Various Visual Classification Tasks Based on Neural Networks,
ICPR00(Vol I: 9-12).
IEEE DOI 0009
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Mingo, L.F.[Luis F.], Arroyo, F.[Fernando], Luengo, C.[Carmen], Castellanos, J.[Juan],
Enhanced Neural Networks and Medical Imaging,
CAIP99(149-156).
Springer DOI 9909
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And:
Learning HyperSurfaces with Neural Networks,
SCIA99(Neural Nets). BibRef

Sugiyama, M., Ogawa, H.,
Exact Incremental Projection Learning in the Presence of Noise,
SCIA99(Neural Nets). BibRef 9900

Morita, S.[Satoru],
Learning Behavior Using Multiresolution Recurrent Neural Network,
CAIP99(157-166).
Springer DOI 9909
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Jahn, H.[Herbert],
Feature Grouping Based on Graphs and Neural Networks,
CAIP99(568-577).
Springer DOI 9909
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Shimodaira, H.[Hiroshi], Keeni, K.[Kanad], Nakayama, K.[Kenji],
Automatic Generation of Initial Weights and Estimation of Hidden Units for Pattern Classification Using Neural Networks,
ICPR98(Vol II: 1568-1571).
IEEE DOI 9808
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Chen, Z.Y., Desai, M.D., and Zhang, X.,
Feedforward Neural Networks with Multilevel Hidden Neurons for Remotely Sensed Image Classification,
ICIP97(II: 653-656).
IEEE DOI BibRef 9700

Gorodnichy, D.O.[Dmitry O.], Reznik, A.M.[Alexandre M.],
Static and dynamic attractors of autoassociative neural networks,
CIAP97(II: 238-245).
Springer DOI 9709
BibRef

Timchenko, L.I.[Leonid I.], Kutaev, Y.F.[Yuri F.], Grudin, M.A.[Maxim A.], Chepornyuk, S.V.[Serge V.], Harvey, D.M.[David M.], Gertsiy, A.A.[Alexander A.],
A brain-like approach to multistage hierarchical image processing,
CIAP97(II: 246-253).
Springer DOI 9709
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Kröner, S.[Sabine],
A structured neural network invariant to cyclic shifts and rotations,
CAIP97(384-391).
Springer DOI 9709
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Foltyniewicz, R.[Rafal],
Efficient high order neural network for rotation, translation and distance invariant recognition of gray scale images,
CAIP95(424-431).
Springer DOI 9509
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Aizenberg, N., Aizenberg, I.N., Krivosheev, G.,
Multi-Valued and Universal Binary Neurons: Mathematical Model, Learning, Networks, Application to Image Processing and Pattern Recognition,
ICPR96(IV: 185-189).
IEEE DOI 9608
(Univ. of Uzhgorod, UKR) BibRef

Michaelis, B., Schnelting, O., Seiffert, U., Mecke, R.,
Adaptive Filtering of Distorted Displacement Vector Fields Using Artificial Neural Networks,
ICPR96(IV: 335-339).
IEEE DOI 9608
(Otto-von-Guericke-Univ., D) BibRef

Michaelis, B.[Bernd], Krell, G.[Gerald],
Artificial neural networks for image improvement,
CAIP93(838-845).
Springer DOI 9309
BibRef

Pereira, M.S., Manolakos, E.S.,
Hierarchical neural network for multiresolution image analysis,
ICIP96(I: 261-264).
IEEE DOI 9610
BibRef

Petkov, N.[Nikolay],
Use of cortical filters and neural networks in a self-organising image classification system,
CIAP95(165-170).
Springer DOI 9509
BibRef

Lin, S.H.[Shang-Hung], Kung, S.Y.,
Probabilistic DBNN via expectation-maximization with multi-sensor classification applications,
ICIP95(III: 236-239).
IEEE DOI 9510
BibRef

Chan, Y., Kung, S.Y.,
Multi-level pixel difference classification methods,
ICIP95(III: 252-255).
IEEE DOI 9510
BibRef

Dunstone, E.S.,
Image processing using an image approximation neural network,
ICIP94(III: 912-916).
IEEE DOI 9411
BibRef

Miyauchi, A., Watanabe, A., Miyauchi, M.,
A method to interpret 3D motion using neural networks,
ICIP94(III: 83-87).
IEEE DOI 9411
BibRef

Biriukov, S.A.,
Spurious states detection and basin describing in feedforward neural networks,
ICPR94(B:586-588).
IEEE DOI 9410
BibRef

Mascarilla, L., Zahzah, E.H., Desachy, J.,
Neural networks classifiers based on geocoded data and multispectral images for satellite image interpretation,
CAIP93(830-837).
Springer DOI 9309
BibRef

Pan, H.P., Forstner, W.,
An MDL-principled evolutionary mechanism to automatic architecturing of pattern recognition neural network,
ICPR92(II:25-28).
IEEE DOI 9208
BibRef

Nedeljkovic, V.,
A novel multilayer neural networks training algorithm that minimizes the probability of classification error,
ICPR92(II:13-16).
IEEE DOI 9208
BibRef

Roy, A.,
On linear programming, neural network design, pattern classification and polynomial time training,
ICPR92(II:5-8).
IEEE DOI 9208
BibRef

Cheng, X.S., Backer, E., Gerbrands, J.J.,
DRBP: dynamically reinforced BP-based ANN-training,
ICPR92(II:9-12).
IEEE DOI 9208
BibRef

Tambouratzis, G.[George], Stonham, T.J.,
A logical neural network that adapts to changes in the pattern environment,
ICPR92(II:46-49).
IEEE DOI 9208
BibRef

Gas, B., Natowicz, R.,
A model of formal neural networks for unsupervised learning of binary temporal sequences,
ICPR92(II:541-544).
IEEE DOI 9208
BibRef

Singer, Y., Yair, E.,
Learning class probabilities from labeled data,
ICPR92(II:553-556).
IEEE DOI 9208
BibRef

Kamata, S.I., Niimi, M., Kawaguchi, E.,
A multi-temporal classification of multi-spectral images using a neural network,
ICPR94(B:470-472).
IEEE DOI 9410
BibRef

Kamata, S.I., Eason, R.O., Perez, A., Kawaguchi, E.,
A neural network classifier for LANDSAT image data,
ICPR92(II:573-576).
IEEE DOI 9208
BibRef

Kamada, H.[Hiroshi],
A proposal for an artificial neural network that optimizes reference vectors: FMNET,
ICPR92(III:590-593).
IEEE DOI 9208
BibRef

Patrikar, A.,
Dual networks and their pattern classification properties,
CVPR91(686-687).
IEEE DOI 0403
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Shapes and Complex Features .


Last update:Nov 11, 2017 at 13:31:57