14.2.5 Semi-Supervised Clustering, Semi-Supervised Learning, Classification

Chapter Contents (Back)
Semi-Supervised. Semi-Supervised Learning. Semi-Supervised Clustering.

Verbeek, J.J.[Jakob J.], Vlassis, N.[Nikos],
Gaussian fields for semi-supervised regression and correspondence learning,
PR(39), No. 10, October 2006, pp. 1864-1875.
WWW Link. Gaussian fields; Regression; Active learning; Model selection 0606
BibRef

Ng, M.K.[Michael K.], Chan, E.Y.[Elaine Y.], So, M.M.C.[Meko M.C.], Ching, W.K.[Wai-Ki],
A semi-supervised regression model for mixed numerical and categorical variables,
PR(40), No. 6, June 2007, pp. 1745-1752.
WWW Link. 0704
Clustering; Regression; Data mining; Numerical variables; Categorical variables BibRef

Song, Y.Q.[Yang-Qiu], Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui],
Semi-supervised sub-manifold discriminant analysis,
PRL(29), No. 13, 1 October 2008, pp. 1806-1813.
WWW Link. 0804
Semi-supervised learning; Dimensionality reduction; Sub-manifold discriminative embedding BibRef

Song, Y.Q.[Yang-Qiu], Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Xiang, S.M.[Shi-Ming],
A unified framework for semi-supervised dimensionality reduction,
PR(41), No. 9, September 2008, pp. 2789-2799.
WWW Link. 0806
Dimensionality reduction; Discriminant analysis, Manifold analysis; Semi-supervised learning BibRef

Angelini, L.[Leonardo], Marinazzo, D.[Daniele], Pellicoro, M.[Mario], Stramaglia, S.[Sebastiano],
Semi-supervised learning by search of optimal target vector,
PRL(29), No. 1, 1 January 2008, pp. 34-39.
WWW Link. 0711
Semi-supervised learning; Kernel principal components; Transductive inference BibRef

Begleiter, R.[Ron], El-Yaniv, R.[Ran], Pechyony, D.[Dmitry],
Repairing self-confident active-transductive learners using systematic exploration,
PRL(29), No. 9, 1 July 2008, pp. 1245-1251.
WWW Link. 0711
Active learning; Transductive learning; Semi-supervised clustering BibRef

Come, E., Oukhellou, L., Denoeux, T., Aknin, P.,
Learning from partially supervised data using mixture models and belief functions,
PR(42), No. 3, March 2009, pp. 334-348.
WWW Link. 0811
Dempster-Shafer theory; Transferable belief model; Mixture models; EM algorithm; Classification; Clustering; Partially supervised learning; Semi-supervised learning BibRef

Wang, J.D.[Jing-Dong], Wang, F.[Fei], Zhang, C.S.[Chang-Shui], Shen, H.C.[Helen C.], Quan, L.[Long],
Linear Neighborhood Propagation and Its Applications,
PAMI(31), No. 9, September 2009, pp. 1600-1615.
IEEE DOI 0907
BibRef

Wang, F.[Fei], Zhang, C.S.[Chang-Shui], Shen, H.C.[Helen C.], Wang, J.D.[Jing-Dong],
Semi-Supervised Classification Using Linear Neighborhood Propagation,
CVPR06(I: 160-167).
IEEE DOI 0606
BibRef

Mallapragada, P.K.[Pavan Kumar], Jin, R.[Rong], Jain, A.K.[Anil K.], Liu, Y.[Yi],
SemiBoost: Boosting for Semi-Supervised Learning,
PAMI(31), No. 11, November 2009, pp. 2000-2014.
IEEE DOI 0910
BibRef
Earlier: A1, A2, A3, Only:
Active query selection for semi-supervised clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Mallapragada, P.K.[Pavan K.], Jin, R.[Rong], Jain, A.K.[Anil K.],
Online visual vocabulary pruning using pairwise constraints,
CVPR10(3073-3080).
IEEE DOI 1006
BibRef

Liu, B., Wang, M., Hong, R., Zha, Z., Hua, X.S.,
Joint Learning of Labels and Distance Metric,
SMC-B(40), No. 3, June 2010, pp. 973-978.
IEEE DOI 1006
Deal with lack of training data and poor distance metrics. Semi-supervised learning. BibRef

Chen, K.[Ke], Wang, S.H.[Shi-Hai],
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions,
PAMI(33), No. 1, January 2011, pp. 129-143.
IEEE DOI 1011
BibRef

Chen, X.M.[Xiao-Ming], Liu, W.Q.[Wan-Quan], Qiu, H.N.[Hui-Ning], Lai, J.H.[Jian-Huang],
APSCAN: A parameter free algorithm for clustering,
PRL(32), No. 7, 1 May 2011, pp. 973-986.
Elsevier DOI 1101
Clustering algorithm; DBSCAN; Affinity propagation algorithm BibRef

Qiu, H.N.[Hui-Ning], Lai, J.H.[Jian-Huang], Huang, J.[Jian], Chen, Y.[Yu],
Semi-supervised discriminant analysis based on UDP regularization,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Adankon, M.M.[Mathias M.], Cheriet, M.[Mohamed],
Help-Training for semi-supervised support vector machines,
PR(44), No. 9, September 2011, pp. 2220-2230.
Elsevier DOI 1106
BibRef
Earlier:
Help-training for semi-supervised discriminative classifiers. Application to SVM,
ICPR08(1-4).
IEEE DOI 0812
Classification; Semi-supervised learning; SVM; Kernel machine See also Optimizing resources in model selection for support vector machine. BibRef

Monaco, J.P., Madabhushi, A.,
Weighted Maximum Posterior Marginals for Random Fields Using an Ensemble of Conditional Densities From Multiple Markov Chain Monte Carlo Simulations,
MedImg(30), No. 7, July 2011, pp. 1353-1364.
IEEE DOI 1107
Adjust when some errors are more important than others. BibRef

Yang, T.[Ting], Priebe, C.E.[Carey E.],
The Effect of Model Misspecification on Semi-Supervised Classification,
PAMI(33), No. 10, October 2011, pp. 2093-2103.
IEEE DOI 1109
Training both on labeled and unlabeled observations. Using unlabeled points degrades result if the model is wrong. BibRef

Jiao, L.C., Shang, F.H.[Fan-Hua], Wang, F.[Fei], Liu, Y.Y.[Yuan-Yuan],
Fast semi-supervised clustering with enhanced spectral embedding,
PR(45), No. 12, December 2012, pp. 4358-4369.
Elsevier DOI 1208
Semi-supervised clustering (SSC); Side information; Spectral embedding; Pairwise constraint; Semantic gap BibRef

Chang, C.C.[Chin-Chun], Chen, H.Y.[Hsin-Yi],
Semi-supervised clustering with discriminative random fields,
PR(45), No. 12, December 2012, pp. 4402-4413.
Elsevier DOI 1208
Semi-supervised clustering; Discriminative random fields BibRef

Liu, W., Wang, J., Chang, S.F.,
Robust and Scalable Graph-Based Semisupervised Learning,
PIEEE(100), No. 9, September 2012, pp. 2624-2638.
IEEE DOI 1209
BibRef

Wang, J.[Jun], Kumar, S.[Sanjiv], Chang, S.F.[Shih-Fu],
Semi-Supervised Hashing for Large-Scale Search,
PAMI(34), No. 12, December 2012, pp. 2393-2406.
IEEE DOI 1210
BibRef

Astudillo, C.A.[César A.], Oommen, B.J.[B. John],
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs,
PR(46), No. 1, January 2013, pp. 293-304.
Elsevier DOI 1209
SOM; Tree-based SOMs; Semi-supervised learning; Pattern recognition BibRef

Joshi, A.J.[Ajay J.], Porikli, F.M.[Fatih M.], Papanikolopoulos, N.P.[Nikolaos P.],
Scalable Active Learning for Multiclass Image Classification,
PAMI(34), No. 11, November 2012, pp. 2259-2273.
IEEE DOI 1209
BibRef
Earlier:
Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback,
CVPR10(2995-3002).
IEEE DOI 1006
BibRef
Earlier:
Multi-class active learning for image classification,
CVPR09(2372-2379).
IEEE DOI 0906
Multi-class learning to avoid large number of training samples. Interaction is only yes-no feedback, not precise category. Hundreds of categories. Scale linearly with size. BibRef

Fu, M., Luo, B., Kong, M.,
Semi-supervised manifold learning based on 2-fold weights,
IET-CV(6), No. 4, 2012, pp. 348-354.
DOI Link 1209
BibRef

Shang, F.H.[Fan-Hua], Jiao, L.C., Liu, Y.Y.[Yuan-Yuan], Tong, H.H.[Hang-Hang],
Semi-supervised learning with nuclear norm regularization,
PR(46), No. 8, August 2013, pp. 2323-2336.
Elsevier DOI 1304
Semi-supervised learning (SSL); Low-rank kernel learning; Graph Laplacian; Nuclear norm regularization; Pairwise constraints BibRef

Czaja, W.[Wojciech], Ehler, M.[Martin],
Schroedinger Eigenmaps for the Analysis of Biomedical Data,
PAMI(35), No. 5, May 2013, pp. 1274-1280.
IEEE DOI 1304
semi-supervised manifold learning BibRef

Dopido, I., Li, J., Marpu, P.R., Plaza, A., Bioucas Dias, J.M., Benediktsson, J.A.,
Semisupervised Self-Learning for Hyperspectral Image Classification,
GeoRS(51), No. 7, 2013, pp. 4032-4044.
IEEE DOI Support vector machines; semisupervised self-learning 1307
BibRef

Guo, X., Huang, X., Zhang, L., Zhang, L., Plaza, A., Benediktsson, J.A.,
Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery,
GeoRS(54), No. 6, June 2016, pp. 3248-3264.
IEEE DOI 1606
geophysical image processing BibRef

Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang], Suen, C.Y.[Ching Y.], Zhu, J.Y.[Jun-Yong],
Multi-Exemplar Affinity Propagation,
PAMI(35), No. 9, 2013, pp. 2223-2237.
IEEE DOI 1307
Affinity Propogation Clustering. MEAP. Belief propagation. BibRef

Huang, G.[Gao], Song, S.[Shiji], Gupta, J.N.D.[Jatinder N.D.], Wu, C.[Cheng],
A second order cone programming approach for semi-supervised learning,
PR(46), No. 12, 2013, pp. 3548-3558.
Elsevier DOI 1308
Semi-supervised learning BibRef

Xing, X.L.[Xiang-Lei], Yu, Y.[Yao], Jiang, H.[Hua], Du, S.[Sidan],
A multi-manifold semi-supervised Gaussian mixture model for pattern classification,
PRL(34), No. 16, 2013, pp. 2118-2125.
Elsevier DOI 1310
Multi-manifold learning BibRef

He, P.[Ping], Xu, X.H.[Xiao-Hua], Hu, K.[Kongfa], Chen, L.[Ling],
Semi-supervised clustering via multi-level random walk,
PR(47), No. 2, 2014, pp. 820-832.
Elsevier DOI 1311
Semi-supervised clustering BibRef

Schwenker, F.[Friedhelm], Trentin, E.[Edmondo],
Partially supervised learning for pattern recognition,
PRL(37), No. 1, 2014, pp. 1-3.
Elsevier DOI 1402
BibRef

Schwenker, F.[Friedhelm], Trentin, E.[Edmondo],
Pattern classification and clustering: A review of partially supervised learning approaches,
PRL(37), No. 1, 2014, pp. 4-14.
Elsevier DOI 1402
Survey, Learning. Partially supervised learning BibRef

Lausser, L.[Ludwig], Schmid, F.[Florian], Schmid, M.[Matthias], Kestler, H.A.[Hans A.],
Unlabeling data can improve classification accuracy,
PRL(37), No. 1, 2014, pp. 15-23.
Elsevier DOI 1402
Partially supervised learning BibRef

Zeyl, T.J.[Timothy J.], Chau, T.[Tom],
A case study of linear classifiers adapted using imperfect labels derived from human event-related potentials,
PRL(37), No. 1, 2014, pp. 54-62.
Elsevier DOI 1402
Partially supervised learning BibRef

Lai, H.P.[Hien Phuong], Visani, M.[Muriel], Boucher, A.[Alain], Ogier, J.M.[Jean-Marc],
A new interactive semi-supervised clustering model for large image database indexing,
PRL(37), No. 1, 2014, pp. 94-106.
Elsevier DOI 1402
Semi-supervised clustering BibRef

Lai, H.P.[Hien Phuong], Visani, M.[Muriel], Boucher, A.[Alain], Ogier, J.M.[Jean-Marc],
Towards an interactive index structuring system for content-based image retrieval in large image databases,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
WWW Link. 1407
Ph.D.. Thesis. BibRef

Soullard, Y.[Yann], Saveski, M.[Martin], Artières, T.[Thierry],
Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models,
PRL(37), No. 1, 2014, pp. 161-171.
Elsevier DOI 1402
Hidden Markov Models BibRef

Castelli, I.[Ilaria], Trentin, E.[Edmondo],
Combination of supervised and unsupervised learning for training the activation functions of neural networks,
PRL(37), No. 1, 2014, pp. 178-191.
Elsevier DOI 1402
Adaptive activation function BibRef

Yoshiyama, K., Sakurai, A.,
Laplacian minimax probability machine,
PRL(37), No. 1, 2014, pp. 192-200.
Elsevier DOI 1402
Semi-supervised learning BibRef

Le, T.B.[Thanh-Binh], Kim, S.W.[Sang-Woon],
On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms,
PRL(41), No. 1, 2014, pp. 53-64.
Elsevier DOI 1403
Semi-supervised learning BibRef

Le, T.B.[Thanh-Binh], Kim, S.W.[Sang-Woon],
Modified criterion to select useful unlabeled data for improving semi-supervised support vector machines,
PRL(60-61), No. 1, 2015, pp. 48-56.
Elsevier DOI 1506
Semi-supervised learning BibRef

Alkama, S.[Sadia], Desquesnes, X.[Xavier], El Moataz, A.[Abderrahim],
Infinity Laplacian on graphs with gradient terms for image and data clustering,
PRL(41), No. 1, 2014, pp. 65-72.
Elsevier DOI 1403
Weighted graphs BibRef

Biswas, A.[Arijit], Jacobs, D.W.[David W.],
Active Image Clustering with Pairwise Constraints from Humans,
IJCV(108), No. 1-2, May 2014, pp. 133-147.
WWW Link. 1405
BibRef
Earlier:
Active image clustering: Seeking constraints from humans to complement algorithms,
CVPR12(2152-2159).
IEEE DOI 1208
BibRef

Biswas, A.[Arijit], Jacobs, D.W.[David W.],
Active subclustering,
CVIU(125), No. 1, 2014, pp. 72-84.
Elsevier DOI 1406
Subclustering BibRef

Wang, H., Li, T., Li, T., Yang, Y.,
Constraint Neighborhood Projections for Semi-Supervised Clustering,
Cyber(44), No. 5, May 2014, pp. 636-643.
IEEE DOI 1405
Algorithm design and analysis BibRef

Xu, J., Wu, Q., Zhang, J., Shen, F., Tang, Z.,
Boosting Separability in Semisupervised Learning for Object Classification,
CirSysVideo(24), No. 7, July 2014, pp. 1197-1208.
IEEE DOI 1407
Algorithm design and analysis BibRef

Daneshpazhouh, A.[Armin], Sami, A.[Ashkan],
Entropy-based outlier detection using semi-supervised approach with few positive examples,
PRL(49), No. 1, 2014, pp. 77-84.
Elsevier DOI 1410
Data mining BibRef

Zhu, X.[Xibin], Schleif, F.M.[Frank-Michael], Hammer, B.[Barbara],
Adaptive conformal semi-supervised vector quantization for dissimilarity data,
PRL(49), No. 1, 2014, pp. 138-145.
Elsevier DOI 1410
Semi-supervised learning BibRef

Schleif, F.M.[Frank-Michael], Zhu, X.[Xibin], Gisbrecht, A.[Andrej], Hammer, B.[Barbara],
Fast approximated relational and kernel clustering,
ICPR12(1229-1232).
WWW Link. 1302
BibRef

Schleif, F.M.[Frank-Michael], Tino, P.[Peter],
Indefinite Core Vector Machine,
PR(71), No. 1, 2017, pp. 187-195.
Elsevier DOI 1707
Indefinite, learning BibRef

Jiang, Y.[Yu], Liu, J.[Jing], Li, Z.C.[Ze-Chao], Lu, H.Q.[Han-Qing],
Semi-supervised Unified Latent Factor learning with multi-view data,
MVA(25), No. 7, October 2014, pp. 1635-1645.
WWW Link.
Springer DOI 1410
BibRef

Wang, S.J.[Shi-Jun], Li, D.[Diana], Petrick, N.[Nicholas], Sahiner, B.[Berkman], Linguraru, M.G.[Marius George], Summers, R.M.[Ronald M.],
Optimizing area under the ROC curve using semi-supervised learning,
PR(48), No. 1, 2015, pp. 276-287.
Elsevier DOI 1410
Receiver operating characteristic BibRef

Tan, K.[Kun], Li, E.[Erzhu], Du, Q.[Qian], Du, P.J.[Pei-Jun],
An efficient semi-supervised classification approach for hyperspectral imagery,
PandRS(97), No. 1, 2014, pp. 36-45.
Elsevier DOI 1410
Hyperspectral BibRef

Tan, K.[Kun], Hu, J.[Jun], Li, J.[Jun], Du, P.J.[Pei-Jun],
A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination,
PandRS(105), No. 1, 2015, pp. 19-29.
Elsevier DOI 1506
Semi-supervised classification BibRef

He, Z.[Zhi], Liu, L.[Lin], Zhou, S.[Suhong], Shen, Y.[Yi],
Learning group-based sparse and low-rank representation for hyperspectral image classification,
PR(60), No. 1, 2016, pp. 1041-1056.
Elsevier DOI 1609
Classification BibRef

He, Z.[Zhi], Li, J.[Jun], Liu, L.[Lin],
Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification,
RS(8), No. 8, 2016, pp. 636.
DOI Link 1609
BibRef

Wang, L.[Liguo], Hao, S.Y.[Si-Yuan], Wang, Q.M.[Qun-Ming], Wang, Y.[Ying],
Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation,
PandRS(97), No. 1, 2014, pp. 123-137.
Elsevier DOI 1410
Hyperspectral imagery BibRef

Lu, Z.W.[Zhi-Wu], Wang, L.W.[Li-Wei],
Noise-robust semi-supervised learning via fast sparse coding,
PR(48), No. 2, 2015, pp. 605-612.
Elsevier DOI 1411
Graph-based semi-supervised learning BibRef

Li, Y.F., Zhou, Z.H.,
Towards Making Unlabeled Data Never Hurt,
PAMI(37), No. 1, January 2015, pp. 175-188.
IEEE DOI 1412
Data models. Unlabelled data hurts classification performance when included. BibRef

Ye, Y.D.[Yang-Dong], Liu, R.[Ruina], Lou, Z.Z.[Zheng-Zheng],
Incorporating side information into multivariate Information Bottleneck for generating alternative clusterings,
PRL(51), No. 1, 2015, pp. 70-78.
Elsevier DOI 1412
Alternative clustering. Find multiple alternative clusterings. BibRef

Shen, F.M.[Fu-Min], Shen, C.H.[Chun-Hua], Shi, Q.F.[Qin-Feng], van den Hengel, A.J.[Anton J.], Tang, Z.M.[Zhen-Min], Shen, H.T.,
Hashing on Nonlinear Manifolds,
IP(24), No. 6, June 2015, pp. 1839-1851.
IEEE DOI 1504
Binary codes BibRef
Earlier: A1, A2, A3, A4, A5, Only:
Inductive Hashing on Manifolds,
CVPR13(1562-1569)
IEEE DOI 1309
hashing; manifold learning Learning based hashing to model the structure in the data. BibRef

Shen, F.M.[Fu-Min], Shen, C.H.[Chun-Hua], Liu, W.[Wei], Shen, H.T.[Heng Tao],
Supervised Discrete Hashing,
CVPR15(37-45)
IEEE DOI 1510
BibRef

Liu, X., Guo, T., He, L., Yang, X.,
A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised Classification,
IP(24), No. 6, June 2015, pp. 1825-1838.
IEEE DOI 1504
Classification algorithms BibRef

Zhao, M.B.[Ming-Bo], Zhan, C.J.[Chou-Jun], Wu, Z.[Zhou], Tang, P.[Peng],
Semi-Supervised Image Classification Based on Local and Global Regression,
SPLetters(22), No. 10, October 2015, pp. 1666-1670.
IEEE DOI 1506
extrapolation BibRef

Wang, D.[Di], Zhang, X.Q.[Xiao-Qin], Fan, M.Y.[Ming-Yu], Ye, X.Z.[Xiu-Zi],
An Efficient Semi-Supervised Classifier Based on Block-Polynomial Mapping,
SPLetters(22), No. 10, October 2015, pp. 1776-1780.
IEEE DOI 1506
computational complexity BibRef

Hong, Y.[Yi], Zhu, W.P.[Wei-Ping],
Spatial co-training for semi-supervised image classification,
PRL(63), No. 1, 2015, pp. 59-65.
Elsevier DOI 1508
Co-training BibRef

Cheng, Y.H.[Yan-Hua], Zhao, X.[Xin], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Semi-supervised learning and feature evaluation for RGB-D object recognition,
CVIU(139), No. 1, 2015, pp. 149-160.
Elsevier DOI 1509
BibRef
Earlier:
Semi-supervised Learning for RGB-D Object Recognition,
ICPR14(2377-2382)
IEEE DOI 1412
RGB-D Accuracy BibRef

Wang, D.[Dong], Yin, Q.[Qiyue], He, R.[Ran], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Semi-supervised subspace segmentation,
ICIP14(2854-2858)
IEEE DOI 1502
Clustering algorithms BibRef

Lu, X.Q.[Xiao-Qiang], Li, X.L.[Xue-Long], Mou, L.C.[Li-Chao],
Semi-Supervised Multitask Learning for Scene Recognition,
Cyber(45), No. 9, September 2015, pp. 1967-1976.
IEEE DOI 1509
feature selection BibRef

Zhuang, L.S.[Lian-Sheng], Gao, S.H.[Sheng-Hua], Tang, J.H.[Jin-Hui], Wang, J.J.[Jing-Jing], Lin, Z.C.[Zhou-Chen], Ma, Y.[Yi], Yu, N.H.[Neng-Hai],
Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features,
IP(24), No. 11, November 2015, pp. 3717-3728.
IEEE DOI 1509
feature extraction BibRef

Zhuang, L.S.[Lian-Sheng], Gao, H.Y.[Hao-Yuan], Lin, Z.C.[Zhou-Chen], Ma, Y.[Yi], Zhang, X.[Xin], Yu, N.H.[Neng-Hai],
Non-Negative Low Rank and Sparse Graph for Semi-Supervised Learning,
CVPR12(2328-2335).
IEEE DOI 1208
BibRef

Zhuang, L.S.[Lian-Sheng], Gao, H.Y.[Hao-Yuan], Huang, J.J.[Jing-Jing], Yu, N.H.[Neng-Hai],
Semi-supervised Classification via Low Rank Graph,
ICIG11(511-516).
IEEE DOI 1109
BibRef

Yang, Y.[Yun], Liu, X.C.[Xing-Chen],
A robust semi-supervised learning approach via mixture of label information,
PRL(68, Part 1), No. 1, 2015, pp. 15-21.
Elsevier DOI 1512
Semi-supervised learning BibRef

Hernández-González, J.[Jerónimo], Inza, I.[Iñaki], Lozano, J.A.[Jose A.],
Weak supervision and other non-standard classification problems: A taxonomy,
PRL(69), No. 1, 2016, pp. 49-55.
Elsevier DOI 1601
Weakly supervised classification BibRef

Loog, M.[Marco],
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification,
PAMI(38), No. 3, March 2016, pp. 462-475.
IEEE DOI 1602
Covariance matrices BibRef

Gan, H.T.[Hai-Tao], Luo, Z.[Zhizeng], Fan, Y.[Yingle], Sang, N.[Nong],
Enhanced manifold regularization for semi-supervised classification,
JOSA-A(33), No. 6, June 2016, pp. 1207-1213.
DOI Link 1606
Pattern recognition BibRef

Zhao, Z.Q.[Zhi-Qiang], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhao, J.Q.[Jia-Qi], Chen, P.H.[Pu-Hua],
Semisupervised Discriminant Feature Learning for SAR Image Category via Sparse Ensemble,
GeoRS(54), No. 6, June 2016, pp. 3532-3547.
IEEE DOI 1606
feature extraction BibRef

Yao, X., Han, J., Cheng, G., Qian, X., Guo, L.,
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning,
GeoRS(54), No. 6, June 2016, pp. 3660-3671.
IEEE DOI 1606
feature extraction BibRef

Amorim, W.P.[Willian P.], Falcão, A.X.[Alexandre X.], Papa, J.P.[João P.], Carvalho, M.H.[Marcelo H.],
Improving semi-supervised learning through optimum connectivity,
PR(60), No. 1, 2016, pp. 72-85.
Elsevier DOI 1609
Semi-supervised learning BibRef

Yu, H.B.[Hong-Bin], Lu, H.T.[Hong-Tao],
Orthogonal optimal reverse prediction for semi-supervised learning,
PR(60), No. 1, 2016, pp. 908-920.
Elsevier DOI 1609
Semi-supervised learning BibRef

Song, J.K.[Jing-Kuan], Gao, L.L.[Lian-Li], Nie, F.P.[Fei-Ping], Shen, H.T.[Heng Tao], Yan, Y.[Yan], Sebe, N.[Nicu],
Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation,
IP(25), No. 11, November 2016, pp. 4999-5011.
IEEE DOI 1610
BibRef
Earlier: A2, A1, A3, A6, A4, A5:
Optimal graph learning with partial tags and multiple features for image and video annotation,
CVPR15(4371-4379)
IEEE DOI 1510
graph theory. Use untagged data to improve supervised learning. BibRef

Song, J.K.[Jing-Kuan], Gao, L.L.[Lian-Li], Zou, F.[Fuhao], Yan, Y.[Yan], Sebe, N.[Nicu],
Deep and fast: Deep learning hashing with semi-supervised graph construction,
IVC(55, Part 2), No. 1, 2016, pp. 101-108.
Elsevier DOI 1612
Deep learning BibRef

Tan, K.[Kun], Zhu, J.[Jishuai], Du, Q.[Qian], Wu, L.X.[Li-Xin], Du, P.J.[Pei-Jun],
A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement,
RS(8), No. 9, 2016, pp. 749.
DOI Link 1610
BibRef

Zhao, Z.[Zhuang], Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.F.[Lian-Fa],
Semi-supervised classification via discriminative sparse manifold regularization,
SP:IC(47), No. 1, 2016, pp. 207-217.
Elsevier DOI 1610
Manifold regularization BibRef

Samat, A.[Alim], Gamba, P.[Paolo], Liu, S.C.[Si-Cong], Du, P.J.[Pei-Jun], Abuduwaili, J.[Jilili],
Jointly Informative and Manifold Structure Representative Sampling Based Active Learning for Remote Sensing Image Classification,
GeoRS(54), No. 11, November 2016, pp. 6803-6817.
IEEE DOI 1610
Kernel BibRef

Liu, W.H.[Wen-He], Gao, C.Q.[Chen-Qiang], Chang, X.J.[Xiao-Jun], Wu, Q.[Qun],
Unified discriminating feature analysis for visual category recognition,
JVCIR(40, Part B), No. 1, 2016, pp. 772-778.
Elsevier DOI 1610
Visual category recognition. Feature selection BibRef

Romaszewski, M.[Michal], Glomb, P.[Przemyslaw], Cholewa, M.[Michal],
Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach,
PandRS(121), No. 1, 2016, pp. 60-76.
Elsevier DOI 1609
Hyperspectral classification BibRef

Dornaika, F., El Traboulsi, Y.,
Matrix exponential based semi-supervised discriminant embedding for image classification,
PR(61), No. 1, 2017, pp. 92-103.
Elsevier DOI 1705
Graph-based semi-supervised learning BibRef

Krijthe, J.H.[Jesse H.], Loog, M.[Marco],
Robust semi-supervised least squares classification by implicit constraints,
PR(63), No. 1, 2017, pp. 115-126.
Elsevier DOI 1612
BibRef
And:
Optimistic semi-supervised least squares classification,
ICPR16(1677-1682)
IEEE DOI 1705
Convergence, Encoding, Labeling, Linear programming, Optimization, Semisupervised learning, Training BibRef

Gu, N., Fan, M., Meng, D.,
Robust Semi-Supervised Classification for Noisy Labels Based on Self-Paced Learning,
SPLetters(23), No. 12, December 2016, pp. 1806-1810.
IEEE DOI 1612
data handling BibRef

Xu, L., Clausi, D.A., Li, F., Wong, A.,
Weakly Supervised Classification of Remotely Sensed Imagery Using Label Constraint and Edge Penalty,
GeoRS(55), No. 3, March 2017, pp. 1424-1436.
IEEE DOI 1703
Correlation BibRef

Zhang, Q.[Qin], Sun, J.[Jianyuan], Zhong, G.[Guoqiang], Dong, J.[Junyu],
Random Multi-Graphs: A semi-supervised learning framework for classification of high dimensional data,
IVC(60), No. 1, 2017, pp. 30-37.
Elsevier DOI 1704
Semi-supervised learning BibRef

Yue, Z.S.[Zong-Sheng], Meng, D.[Deyu], He, J.[Juan], Zhang, G.[Gemeng],
Semi-supervised learning through adaptive Laplacian graph trimming,
IVC(60), No. 1, 2017, pp. 38-47.
Elsevier DOI 1704
Semi-supervised learning BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Su, H.J.[Hong-Jun], Zhou, S.G.[Shao-Guang],
Discriminative Sparse Representation for Hyperspectral Image Classification: A Semi-Supervised Perspective,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Wang, X.D.[Xiao-Dong], Chen, R.C.[Rung-Ching], Hong, C.Q.[Chao-Qun], Zeng, Z.Q.[Zhi-Qiang], Zhou, Z.L.[Zhi-Li],
Semi-supervised multi-label feature selection via label correlation analysis with L1-norm graph embedding,
IVC(63), No. 1, 2017, pp. 10-23.
Elsevier DOI 1706
Semi-supervised, learning BibRef

Tang, P.[Peng], Wang, X.G.[Xing-Gang], Huang, Z.[Zilong], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu],
Deep patch learning for weakly supervised object classification and discovery,
PR(71), No. 1, 2017, pp. 446-459.
Elsevier DOI 1707
Patch, feature, learning BibRef


Jacobs, M.[Matt],
A Fast MBO Scheme for Multiclass Data Classification,
SSVM17(335-347).
Springer DOI 1706
semi-supervised data classification BibRef

Salinas-Gutiérrez, R.[Rogelio], Hernández-Quintero, A.[Angélica], Dalmau-Cedeño, O.[Oscar], Pérez-Díaz, Á.P.[Ángela Paulina],
Modeling Dependencies in Supervised Classification,
MCPR17(117-126).
Springer DOI 1706
BibRef

Yang, Y., Loog, M.[Marco],
Active learning using uncertainty information,
ICPR16(2646-2651)
IEEE DOI 1705
Labeling, Learning systems, Linear programming, Logistics, Measurement uncertainty, Pattern recognition, Uncertainty BibRef

Mey, A.[Alexander], Loog, M.[Marco],
A soft-labeled self-training approach,
ICPR16(2604-2609)
IEEE DOI 1705
Labeling, Linear programming, Mathematical model, Minimization, Pattern recognition, Probability distribution, Risk, management BibRef

Valev, V., Yanev, N., Krzyzak, A.,
A new geometrical approach for solving the supervised pattern recognition problem,
ICPR16(1648-1652)
IEEE DOI 1705
Color, Complexity theory, Optimization, Partitioning algorithms, Pattern recognition, Training BibRef

Hou, C.Q.[Cui-Qin], Xia, Y.J.[Ying-Ju], Xu, Z.R.[Zhuo-Ran], Sun, J.[Jun],
Semi-supervised learning competence of classifiers based on graph for dynamic classifier selection,
ICPR16(3650-3654)
IEEE DOI 1705
Classification algorithms, Heuristic algorithms, Optimization, Pattern recognition, Testing, Training, Training data, competence, dynamic classifier selection, ensemble learning, graph, semi-supervised BibRef

Robles-Kelly, A., Wei, R.[Ran],
Semi-supervised image labelling using barycentric graph embeddings,
ICPR16(1518-1523)
IEEE DOI 1705
Cost function, Eigenvalues and eigenfunctions, Image color analysis, Image edge detection, Labeling, Laplace equations, Mathematical, model BibRef

Oh, B.[Byonghwa], Yang, J.[Jihoon],
Enhancing label inference algorithms considering vertex importance in graph-based semi-supervised learning,
ICPR16(1671-1676)
IEEE DOI 1705
Algorithm design and analysis, Convergence, Heuristic algorithms, Inference algorithms, Manifolds, Prediction algorithms, Supervised, learning BibRef

Sousa, C.A.R.[Celso A. R.], Batista, G.E.A.P.A.[Gustavo E.A.P.A.],
Constrained Local and Global Consistency for semi-supervised learning,
ICPR16(1689-1694)
IEEE DOI 1705
Benchmark testing, Closed-form solutions, Convex functions, Laplace equations, Optimization, Robustness, Semisupervised, learning BibRef

Fu, Y.W.[Yan-Wei], Sigal, L.[Leonid],
Semi-supervised Vocabulary-Informed Learning,
CVPR16(5337-5346)
IEEE DOI 1612
BibRef

Krijthe, J.H.[Jesse H.], Loog, M.[Marco],
The Peaking Phenomenon in Semi-supervised Learning,
SSSPR16(299-309).
Springer DOI 1611
BibRef

Su, H.[Hang], Zhu, J.[Jun], Yin, Z.Z.[Zhao-Zheng], Dong, Y.P.[Yin-Peng], Zhang, B.[Bo],
Efficient and Robust Semi-supervised Learning Over a Sparse-Regularized Graph,
ECCV16(VIII: 583-598).
Springer DOI 1611
BibRef

Kim, K.I.[Kwang In],
Semi-supervised Learning Based on Joint Diffusion of Graph Functions and Laplacians,
ECCV16(V: 713-729).
Springer DOI 1611
BibRef

Joulin, A.[Armand], van der Maaten, L.[Laurens], Jabri, A.[Allan], Vasilache, N.[Nicolas],
Learning Visual Features from Large Weakly Supervised Data,
ECCV16(VII: 67-84).
Springer DOI 1611
BibRef

Wan, F.[Fang], Wei, P.X.[Peng-Xu], Han, Z.J.[Zhen-Jun], Fu, K.[Kun], Ye, Q.X.[Qi-Xiang],
Weakly supervised object detection with correlation and part suppression,
ICIP16(3638-3642)
IEEE DOI 1610
Correlation. Find and uses strong negative samples. BibRef

Gangeh, M.J.[Mehrdad J.], Bedawi, S.M.A.[Safaa M. A.], Ghodsi, A.[Ali], Karray, F.[Fakhri],
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion,
ICIAR16(12-19).
Springer DOI 1608
BibRef

Li, C.G.[Chun-Guang], Lin, Z.C.[Zhou-Chen], Zhang, H.G.[Hong-Gang], Guo, J.[Jun],
Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning,
ICCV15(2767-2775)
IEEE DOI 1602
BibRef

Wang, X.B.[Xiao-Bo], Guo, X.J.[Xiao-Jie], Li, S.Z.[Stan Z.],
Adaptively Unified Semi-Supervised Dictionary Learning with Active Points,
ICCV15(1787-1795)
IEEE DOI 1602
Data models BibRef

Shi, X.[Xin], Zhang, C.[Chao], Wei, F.Y.[Fang-Yun], Zhang, H.Y.[Hong-Yang], She, Y.Y.[Yi-Yuan],
Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yin, Q.[Qiyue], Wu, S.[Shu], Wang, L.[Liang],
Partially tagged image clustering,
ICIP15(4012-4016)
IEEE DOI 1512
Image clustering BibRef

Yang, H.X.[Hong-Xue], Kong, X.W.[Xiang-Wei], Fu, H.Y.[Hai-Yan], Li, M.[Ming], Zhao, G.P.[Gen-Ping],
Semi-supervised learning based on group sparse for relative attributes,
ICIP15(3931-3935)
IEEE DOI 1512
Group sparse; labeling; relative attributes; semi-supervised learning BibRef

Ye, Z.P.[Zhi-Peng], Liu, P.[Peng], Tang, X.L.[Xiang-Long], Zhao, W.[Wei],
May the torcher light our way: A negative-accelerated active learning framework for image classification,
ICIP15(1658-1662)
IEEE DOI 1512
Image classification BibRef

Cardona, H.D.V.[Hernán Darío Vargas], Álvarez, M.A.[Mauricio A.], Orozco, Á.A.[Álvaro A.],
Convolved Multi-output Gaussian Processes for Semi-Supervised Learning,
CIAP15(I:109-118).
Springer DOI 1511
BibRef

Gómez-González, S.[Sebastián], Álvarez, M.A.[Mauricio A.], García, H.F.[Hernan F.], Ríos, J.I.[Jorge I.], Orozco, A.A.[Alvaro A.],
Discriminative Training for Convolved Multiple-Output Gaussian Processes,
CIARP15(595-602).
Springer DOI 1511
BibRef

Peikari, M.[Mohammad], Zubovits, J.[Judit], Clarke, G.[Gina], Martel, A.L.[Anne L.],
Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology,
MLMI15(263-270).
Springer DOI 1511
BibRef

Wang, Z.H.[Zi-Heng], Ji, Q.A.[Qi-Ang],
Classifier learning with hidden information,
CVPR15(4969-4977)
IEEE DOI 1510
BibRef

Wigness, M.[Maggie], Draper, B.A.[Bruce A.], Beveridge, J.R.[J. Ross],
Efficient label collection for unlabeled image datasets,
CVPR15(4594-4602)
IEEE DOI 1510
BibRef

Kim, K.I.[Kwang In], Tompkin, J.[James], Pfister, H.[Hanspeter], Theobalt, C.[Christian],
Semi-supervised learning with explicit relationship regularization,
CVPR15(2188-2196)
IEEE DOI 1510
BibRef

Wuttke, S., Middelmann, W., Stilla, U.,
Concept for a Compound Analysis in Active Learning for Remote Sensing,
PIA15(273-279).
DOI Link 1504
BibRef

Pham, H.D.[Hien Duy], Kim, K.H.[Kye-Hyeon], Choi, S.J.[Seung-Jin],
Semi-supervised Learning on Bi-relational Graph for Image Annotation,
ICPR14(2465-2470)
IEEE DOI 1412
Computational modeling BibRef

Berton, L.[Lilian], de Andrade Lopes, A.[Alneu],
Graph Construction Based on Labeled Instances for Semi-supervised Learning,
ICPR14(2477-2482)
IEEE DOI 1412
Accuracy BibRef

Liu, P.C.[Peng-Cheng], Yang, P.[Peipei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu], Hao, H.W.[Hong-Wei],
Semi-supervised Learning for Cross-Device Visual Location Recognition,
ICPR14(2873-2878)
IEEE DOI 1412
Cameras BibRef

Yang, Y.[Yi], Newsam, S.[Shawn],
Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration,
ICPR14(4062-4067)
IEEE DOI 1412
Computational modeling BibRef

Aodha, O.M.[Oisin Mac], Campbell, N.D.F.[Neill D.F.], Kautz, J.[Jan], Brostow, G.J.[Gabriel J.],
Hierarchical Subquery Evaluation for Active Learning on a Graph,
CVPR14(564-571)
IEEE DOI 1409
active learning; semi-supervised learning BibRef

Niu, Z.X.[Zhen-Xing], Hua, G.[Gang], Gao, X.B.[Xin-Bo], Tian, Q.[Qi],
Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media,
CVPR14(4233-4240)
IEEE DOI 1409
Image recognition; Social Media; Tag; Topic Model BibRef

Lai, H.J.[Han-Jiang], Pan, Y.[Yan], Lu, C.[Canyi], Tang, Y.[Yong], Yan, S.C.[Shui-Cheng],
Efficient k-Support Matrix Pursuit,
ECCV14(II: 617-631).
Springer DOI 1408
semi-supervised classification BibRef

Lad, S.[Shrenik], Parikh, D.[Devi],
Interactively Guiding Semi-Supervised Clustering via Attribute-Based Explanations,
ECCV14(VI: 333-349).
Springer DOI 1408
BibRef

Wigness, M.[Maggie], Draper, B.A.[Bruce A.], Beveridge, J.R.[J. Ross],
Selectively guiding visual concept discovery,
WACV14(247-254)
IEEE DOI 1406
Selective Guidance. Give some guidance, cluster to find concepts. Accuracy BibRef

Kim, K.I.[Kwang In], Tompkin, J.[James], Theobalt, C.[Christian],
Curvature-Aware Regularization on Riemannian Submanifolds,
ICCV13(881-888)
IEEE DOI 1403
Semi-supervised learning; manifold; regularization BibRef

Xu, J.S.[Jing-Song], Wu, Q.A.[Qi-Ang], Zhang, J.[Jian], Shen, F.M.[Fu-Min], Tang, Z.M.[Zhen-Min],
Training boosting-like algorithms with semi-supervised subspace learning,
ICIP13(4302-4306)
IEEE DOI 1402
AdaBoost BibRef

Xie, W.X.[Wen-Xuan], Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin], Xiao, J.G.[Jian-Guo],
Multimodal semi-supervised image classification by combining tag refinement, graph-based learning and support vector regression,
ICIP13(4307-4311)
IEEE DOI 1402
Graph-based semi-supervised learning BibRef

Hoai, M.[Minh], Zisserman, A.[Andrew],
Discriminative Sub-categorization,
CVPR13(1666-1673)
IEEE DOI 1309
Weakly supervised with positive and negative examples. BibRef

Asafi, S.[Shmuel], Cohen-Or, D.[Daniel],
Constraints as Features,
CVPR13(1634-1641)
IEEE DOI 1309
clustering; image segmentation; machine learning; semi-supervised Constrained clustering using cannot-link information. BibRef

Turakhia, N.[Naman], Parikh, D.[Devi],
Attribute Dominance: What Pops Out?,
ICCV13(1225-1232)
IEEE DOI 1403
attribute based classification BibRef

Biswas, A.[Arijit], Parikh, D.[Devi],
Simultaneous Active Learning of Classifiers: Attributes via Relative Feedback,
CVPR13(644-651)
IEEE DOI 1309
Active Learning. Reduce annotation costs. BibRef

Shi, L.[Lei], Khushaba, R., Kodagoda, S., Dissanayake, G.,
Application of CRF and SVM based semi-supervised learning for semantic labeling of environments,
ICARCV12(835-840).
IEEE DOI 1304
BibRef

Ebert, S.[Sandra], Fritz, M.[Mario], Schiele, B.[Bernt],
Semi-Supervised Learning on a Budget: Scaling Up to Large Datasets,
ACCV12(I:232-245).
Springer DOI 1304
BibRef

Zhang, T.Z.[Tian-Zhu], Cai, R.[Rui], Li, Z.W.[Zhi-Wei], Zhang, L.[Lei], Lu, H.Q.[Han-Qing],
Hierarchical Object Representations for Visual Recognition via Weakly Supervised Learning,
ACCV12(I:474-485).
Springer DOI 1304
BibRef

Cao, C.[Chen], Chen, S.F.[Shi-Feng], Zou, C.Q.[Chang-Qing], Liu, J.Z.[Jian-Zhuang],
Locating high-density clusters with noisy queries,
ICPR12(3537-3540).
WWW Link. 1302
BibRef

Hido, S.[Shohei], Kashima, H.[Hisashi],
Hash-based structural similarity for semi-supervised Learning on attribute graphs,
ICPR12(3009-3012).
WWW Link. 1302
BibRef

Lopresti, D.P.[Daniel P.], Nagy, G.[George],
Optimal data partition for semi-automated labeling,
ICPR12(286-289).
WWW Link. 1302
BibRef

Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
Efficient Similarity Derived from Kernel-Based Transition Probability,
ECCV12(VI: 371-385).
Springer DOI 1210
BibRef

Siva, P.[Parthipan], Russell, C.[Chris], Xiang, T.[Tao],
In Defence of Negative Mining for Annotating Weakly Labelled Data,
ECCV12(III: 594-608).
Springer DOI 1210
Annotate based on examples that have not occurred previously. BibRef

Shrivastava, A.[Abhinav], Singh, S.[Saurabh], Gupta, A.[Abhinav],
Constrained Semi-Supervised Learning Using Attributes and Comparative Attributes,
ECCV12(III: 369-383).
Springer DOI 1210
BibRef

Ahumada, H.C.[Hernán C.], Granitto, P.M.[Pablo M.],
A Simple Hybrid Method for Semi-supervised Learning,
CIARP12(138-145).
Springer DOI 1209
BibRef

Duvenaud, D.[David], Marlin, B.[Benjamin], Murphy, K.[Kevin],
Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification,
CRV11(371-378).
IEEE DOI 1105
BibRef

Kimura, A.[Akisato], Kameoka, H.[Hirokazu], Sugiyama, M.[Masashi], Nakano, T.[Takuho], Maeda, E.[Eisaku], Sakano, H.[Hitoshi], Ishiguro, K.[Katsuhiko],
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations,
ICPR10(2933-2936).
IEEE DOI 1008
BibRef

Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei], Ruan, X.[Xiang],
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition,
ICPR10(3045-3048).
IEEE DOI 1008
BibRef

Chandel, A.S.[Arvind Singh], Tiwari, A.[Aruna], Chaudhari, N.S.[Narendra S.],
Constructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining,
PReMI09(62-67).
Springer DOI 0912
BibRef

Gui, J.[Jie], Huang, D.S.[De-Shuang], You, Z.H.[Zhu-Hong],
An improvement on learning with local and global consistency,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Fu, Y.[Yun], Li, Z.[Zhu], Zhou, X.[Xi], Huang, T.S.[Thomas S.],
Laplacian Affinity Propagation for Semi-Supervised Object Classification,
ICIP07(I: 189-192).
IEEE DOI 0709
graph-based learning algorithm BibRef

Yang, W.[Wuyi], Zhang, S.[Shuwu], Liang, W.[Wei],
A Graph Based Subspace Semi-supervised Learning Framework for Dimensionality Reduction,
ECCV08(II: 664-677).
Springer DOI 0810
BibRef

Zhang, Z.Y.[Zhen-Yue], Zha, H.Y.[Hong-Yuan], Zhang, M.[Min],
Spectral methods for semi-supervised manifold learning,
CVPR08(1-6).
IEEE DOI 0806
BibRef

Gong, Y.C.[Yun-Chao], Chen, C.L.[Chuan-Liang], Tian, Y.J.[Yin-Jie],
Graph-based semi-supervised learning with redundant views,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Korecki, J.N.[John N.], Banfield, R.E.[Robert E.], Hall, L.O.[Lawrence O.], Bowyer, K.W.[Kevin W.], Kegelmeyer, W.P.[W. Philip],
Semi-supervised learning on large complex simulations,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Hu, J.Y.[Jian-Ying], Singh, M.[Moninder], Mojsilovic, A.[Aleksandra],
Categorization using semi-supervised clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Cheng, Y.B.[Yu-Bo], Cai, Y.P.[Yun-Peng], Sun, Y.J.[Yi-Jun], Li, J.[Jian],
Semi-supervised feature selection under logistic I-RELIEF framework,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Xiao, R.[Rui], Shi, P.F.[Peng-Fei],
Semi-supervised marginal discriminant analysis based on QR decomposition,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Tahir, M.A.[Muhammad Atif], Smith, J.E.[James E.], Caleb-Solly, P.[Praminda],
A Novel Feature Selection Based Semi-supervised Method for Image Classification,
CVS08(xx-yy).
Springer DOI 0805
See also Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. BibRef

Liu, R.J.[Ru-Jie], Wang, Y.H.[Yue-Hong], Baba, T.[Takayuki], Masumoto, D.[Daiki],
Semi-supervised learning by locally linear embedding in kernel space,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Batra, D., Sukthankar, R., Chen, T.,
Semi-Supervised Clustering via Learnt Codeword Distances,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Marin-Castro, H.[Heidy], Sucar, L.E.[L. Enrique], Morales, E.[Eduardo],
Automatic Image Annotation Using a Semi-supervised Ensemble of Classifiers,
CIARP07(487-495).
Springer DOI 0711
BibRef

Didaci, L.[Luca], Fumera, G.[Giorgio], Roli, F.[Fabio],
Analysis of Co-training Algorithm with Very Small Training Sets,
SSSPR12(719-726).
Springer DOI 1211
BibRef

Didaci, L.[Luca], Roli, F.[Fabio],
Using Co-training and Self-training in Semi-supervised Multiple Classifier Systems,
SSPR06(522-530).
Springer DOI 0608
BibRef

Zhang, R.[Rong], Rudnicky, A.I.[Alexander I.],
A New Data Selection Principle for Semi-Supervised Incremental Learning,
ICPR06(II: 780-783).
IEEE DOI 0609
BibRef

Gong, H.F.[Hai-Feng], Pan, C.H.[Chun-Hong], Yang, Q.[Qing], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Neural Network Modeling of Spectral Embedding,
BMVC06(I:227).
PDF File. 0609
BibRef
Earlier:
A Semi-Supervised Framework for Mapping Data to the Intrinsic Manifold,
ICCV05(I: 98-105).
IEEE DOI 0510
Reduce dimensionality, but to the intrinsic form. BibRef

Duan, R.[Rong], Jiang, W.[Wei], Man, H.[Hong],
Semi-Supervised Image Classification in Likelihood Space,
ICIP06(957-960).
IEEE DOI 0610
BibRef
And:
Robust Adjusted Likelihood Function for Image Analysis,
AIPR06(29-29).
IEEE DOI 0610
BibRef

Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui], Lee, J.G.[Jian-Guo],
Graph Based Multi-class Semi-supervised Learning Using Gaussian Process,
SSPR06(450-458).
Springer DOI 0608
BibRef

Rosenberg, C.[Chuck], Hebert, M.[Martial], Schneiderman, H.[Henry],
Semi-Supervised Self-Training of Object Detection Models,
WACV05(I: 29-36).
IEEE DOI 0502
BibRef

Rosenberg, C., Hebert, M.,
Training Object Detection Models with Weakly Labeled Data,
BMVC02(Poster Session). 0208
BibRef

Saint-Jean, C., Frelicot, C.,
A robust semi-supervised EM-based clustering algorithm with a reject option,
ICPR02(III: 399-402).
IEEE DOI 0211
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Iterative, Hierarchical Clustering Techniques .


Last update:Sep 18, 2017 at 11:34:11