Takiyama, R.[Ryuzo],
A general method for training the committee machine,
PR(10), No. 4, 1978, pp. 255-259.
Elsevier DOI
0309
BibRef
Takiyama, R.[Ryuzo],
A two-level committee machine: a representation and a learning
procedure for general piecewise linear discriminant functions,
PR(13), No. 3, 1981, pp. 269-274.
Elsevier DOI
0309
BibRef
Takiyama, R.[Ryuzo],
A committee machine with a set of networks composed of two
single-threshold elements as committee members,
PR(15), No. 5, 1982, pp. 405-412.
Elsevier DOI
0309
BibRef
El-Shishini, H.,
Abdel-Mottaleb, M.S.,
El-Raey, M.,
Shoukry, A.,
A Multistage Algorithm for Fast Classification of Patterns,
PRL(10), No. 4, 1989, pp. 211-215.
BibRef
8900
Giusti, N.[Nicola],
Masulli, F.[Francesco],
Sperduti, A.[Alessandro],
Theoretical and Experimental Analysis of a Two-Stage System for
Classification,
PAMI(24), No. 7, July 2002, pp. 893-904.
IEEE Abstract.
0207
Global classifier with rejection followed by local, nearest neighbor
classification.
BibRef
Vuurpijl, L.[Louis],
Schomaker, L.[Lambert],
van Erp, M.[Merijn],
Architectures for Detecting and Solving Conflicts:
Two-Stage Classification and Support Vector Classifiers,
IJDAR(5), No. 4, July 2003, pp. 213-223.
Springer DOI
0308
BibRef
Earlier: A3, A1, A2:
An overview and comparison of voting methods for pattern recognition,
FHR02(195-200).
IEEE Top Reference.
0209
BibRef
Quost, B.[Benjamin],
Denoeux, T.[Thierry],
Masson, M.H.[Marie-Helene],
Pairwise classifier combination using belief functions,
PRL(28), No. 5, 1 April 2007, pp. 644-653.
Elsevier DOI
0703
Polychotomous classification; Dempster-Shafer theory; Evidence theory;
Classification; Classifier fusion
BibRef
Quost, B.[Benjamin],
Destercke, S.[Sébastien],
Classification by pairwise coupling of imprecise probabilities,
PR(77), 2018, pp. 412-425.
Elsevier DOI
1802
Classifier combination, Reasoning under uncertainty, Cautious predictions
BibRef
Ko, A.H.R.[Albert Hung-Ren],
Sabourin, Jr., R.[Robert],
de Souza Britto, A.[Alceu],
Soares de Oliveira, L.E.[Luiz E.],
Pairwise fusion matrix for combining classifiers,
PR(40), No. 8, August 2007, pp. 2198-2210.
Elsevier DOI
0704
BibRef
Earlier: A1, A2, A3, Only:
A New Objective Function for Ensemble Selection in Random Subspaces,
ICPR06(IV: 185-188).
IEEE DOI
0609
Fusion function; Combining classifiers; Confusion matrix;
Pattern recognition; Majority voting; Ensemble of learning machines
BibRef
Ko, A.H.R.[Albert Hung-Ren],
Sabourin, Jr., R.[Robert],
de Souza Britto, A.[Alceu],
From dynamic classifier selection to dynamic ensemble selection,
PR(41), No. 5, May 2008, pp. 1735-1748.
Elsevier DOI
0711
BibRef
Earlier:
K-Nearest Oracle for Dynamic Ensemble Selection,
ICDAR07(422-426).
IEEE DOI
0709
Oracle; Combining classifiers; Classifier selection; Ensemble selection;
Pattern recognition; Majority voting; Ensemble of learning machines
BibRef
Cruz, R.M.O.,
Zakane, H.H.,
Sabourin, Jr., R.[Robert],
Cavalcanti, G.D.C.,
Dynamic ensemble selection VS K-NN: Why and when dynamic selection
obtains higher classification performance?,
IPTA17(1-6)
IEEE DOI
1804
learning (artificial intelligence), pattern classification,
DS methods, K -nearest Neighbors, K-NN classifier reside,
K-nearest neighbors
BibRef
dos Santos, E.M.[Eulanda M.],
Sabourin, R.[Robert],
Maupin, P.[Patrick],
A dynamic overproduce-and-choose strategy for the selection of
classifier ensembles,
PR(41), No. 10, October 2008, pp. 2993-3009.
Elsevier DOI
0808
Overproduce-and-choose strategy; Dynamic classifier selection;
Optimization; Measures of confidence
BibRef
Ko, A.H.R.[Albert Hung-Ren],
Sabourin, Jr., R.[Robert],
Soares de Oliveira, L.E.[Luiz E.],
de Souza Britto, A.[Alceu],
The implication of data diversity for a classifier-free ensemble
selection in random subspaces,
ICPR08(1-5).
IEEE DOI
0812
BibRef
Ayad, H.G.[Hanan G.],
Kamel, M.S.[Mohamed S.],
Cumulative Voting Consensus Method for Partitions with Variable Number
of Clusters,
PAMI(30), No. 1, January 2008, pp. 160-173.
IEEE DOI
0711
BibRef
Ayad, H.G.[Hanan G.],
Kamel, M.S.[Mohamed S.],
On voting-based consensus of cluster ensembles,
PR(43), No. 5, May 2010, pp. 1943-1953.
Elsevier DOI
1003
Clustering; Cluster ensembles; Voting-based consensus
BibRef
Wu, J.X.[Jian-Xin],
Brubaker, S.C.[S. Charles],
Mullin, M.D.[Matthew D.],
Rehg, J.M.[James M.],
Fast Asymmetric Learning for Cascade Face Detection,
PAMI(30), No. 3, March 2008, pp. 369-382.
IEEE DOI
0801
Face Detection. Separate feature selection and classifier ensemble formation.
BibRef
Brubaker, S.C.[S. Charles],
Mullin, M.D.[Matthew D.],
Rehg, J.M.[James M.],
Towards Optimal Training of Cascaded Detectors,
ECCV06(I: 325-337).
Springer DOI
0608
Face recognition. Analysis of the technique.
BibRef
Brubaker, S.C.[S. Charles],
Wu, J.X.[Jian-Xin],
Sun, J.[Jie],
Mullin, M.D.[Matthew D.],
Rehg, J.M.[James M.],
Towards the Optimal Training of Cascades of Boosted Ensembles,
On the Design of Cascades of Boosted Ensembles for Face Detection,
IJCV(77), No. 1-3, May 2008, pp. 65-86.
Springer DOI
0803
BibRef
Earlier:
CLOR06(301-320).
Springer DOI
0711
BibRef
Hore, P.[Prodip],
Hall, L.O.[Lawrence O.],
Goldgof, D.B.[Dmitry B.],
A scalable framework for cluster ensembles,
PR(42), No. 5, May 2009, pp. 676-688.
Elsevier DOI
0902
Clustering; Hard/fuzzy-k-means; Large data sets; Ensemble;
Scalability; Single pass algorithm
See also generic knowledge-guided image segmentation and labeling system using fuzzy clustering algorithms, A.
BibRef
Rodriguez, J.J.[Juan J.],
Garcia-Osorio, C.[Cesar],
Maudes, J.[Jesus],
Forests of nested dichotomies,
PRL(31), No. 2, 15 January 2010, pp. 125-132.
Elsevier DOI
1001
Nested dichotomies; Classifier ensembles; Multiclass classification;
Decision trees
BibRef
Foo, B.,
van der Schaar, M.,
A Distributed Approach for Optimizing Cascaded Classifier Topologies in
Real-Time Stream Mining Systems,
IP(19), No. 11, November 2010, pp. 3035-3048.
IEEE DOI
1011
Configure classifiers in real-time.
BibRef
Hullermeier, E.[Eyke],
Vanderlooy, S.[Stijn],
Combining predictions in pairwise classification:
An optimal adaptive voting strategy and its relation to weighted voting,
PR(43), No. 1, January 2010, pp. 128-142.
Elsevier DOI
0909
Learning by pairwise comparison; Label ranking; Aggregation
strategies; Classifier combination; Weighted voting; MAP prediction
BibRef
Galar, M.[Mikel],
Fernandez, A.[Alberto],
Barrenechea, E.[Edurne],
Bustince, H.[Humberto],
Herrera, F.[Francisco],
An overview of ensemble methods for binary classifiers in multi-class
problems: Experimental study on one-vs-one and one-vs-all schemes,
PR(44), No. 8, August 2011, pp. 1761-1776.
Elsevier DOI
1104
Survey, Ensemble Clustering. Multi-classification; Pairwise learning; One-vs-one; One-vs-all;
Decomposition strategies; Ensembles
BibRef
Galar, M.[Mikel],
Fernández, A.[Alberto],
Barrenechea, E.[Edurne],
Bustince, H.[Humberto],
Herrera, F.[Francisco],
Dynamic classifier selection for One-vs-One strategy:
Avoiding non-competent classifiers,
PR(46), No. 12, 2013, pp. 3412-3424.
Elsevier DOI
1308
Multi-classification
See also EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling.
BibRef
Krawczyk, B.[Bartosz],
Galar, M.[Mikel],
Wozniak, M.[Michal],
Bustince, H.[Humberto],
Herrera, F.[Francisco],
Dynamic ensemble selection for multi-class classification with
one-class classifiers,
PR(83), 2018, pp. 34-51.
Elsevier DOI
1808
Machine learning, Classifier ensemble,
One-class classification, Multi-class decomposition,
Ensemble pruning
BibRef
Galar, M.[Mikel],
Fernández, A.[Alberto],
Barrenechea, E.[Edurne],
Herrera, F.[Francisco],
DRCW-OVO: Distance-based relative competence weighting combination
for One-vs-One strategy in multi-class problems,
PR(48), No. 1, 2015, pp. 28-42.
Elsevier DOI
1410
Multi-class classification
BibRef
Foo, B.,
Turaga, D.S.,
Verscheure, O.,
van der Schaar, M.,
Amini, L.,
Configuring Trees of Classifiers in Distributed Multimedia Stream
Mining Systems,
CirSysVideo(21), No. 3, March 2011, pp. 245-258.
IEEE DOI
1104
BibRef
Visentini, I.[Ingrid],
Snidaro, L.[Lauro],
Foresti, G.L.[Gian Luca],
Cascaded online boosting,
RealTimeIP(5), No. 4, December 2010, pp. 245-257.
WWW Link.
1101
BibRef
Earlier:
On-line boosted cascade for object detection,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Feitosa, R.Q.[Raul Queiroz],
da Costa, G.A.O.P.[Gilson Alexandre Ostwald Pedro],
Mota, G.L.A.[Guilherme Lucio Abelha],
Feijo, B.[Bruno],
Modeling alternatives for fuzzy Markov chain-based classification of
multitemporal remote sensing data,
PRL(32), No. 7, 1 May 2011, pp. 927-940.
Elsevier DOI
1101
Classification; Multitemporal image analysis; Fuzzy Markov chain
BibRef
Zhang, C.X.[Chun-Xia],
Duin, R.P.W.[Robert P.W.],
An experimental study of one- and two-level classifier fusion for
different sample sizes,
PRL(32), No. 14, 15 October 2011, pp. 1756-1767.
Elsevier DOI
1110
Ensemble classifier; Classifier fusion rule; Training sample size;
Fixed combiner; Trainable combiner
BibRef
Li, Y.[Yan],
Tax, D.M.J.[David M.J.],
Duin, R.P.W.[Robert P.W.],
Loog, M.[Marco],
Multiple-instance learning as a classifier combining problem,
PR(46), No. 3, March 2013, pp. 865-874.
Elsevier DOI
1212
Multiple instance learning; Classifier combining
BibRef
Cheplygina, V.[Veronika],
Tax, D.M.J.[David M.J.],
Loog, M.[Marco],
Does one rotten apple spoil the whole barrel?,
ICPR12(1156-1159).
WWW Link.
1302
Multiple Instance Learning.
BibRef
Yang, Y.Z.[Ya-Zhou],
Loog, M.[Marco],
A variance maximization criterion for active learning,
PR(78), 2018, pp. 358-370.
Elsevier DOI
1804
BibRef
Earlier:
Active learning using uncertainty information,
ICPR16(2646-2651)
IEEE DOI
1705
BibRef
And: A2, A2:
An empirical investigation into the inconsistency of sequential
active learning,
ICPR16(210-215)
IEEE DOI
1705
Labeling, Linear programming,
Measurement uncertainty, Uncertainty
Active learning, Retraining information matrix, Variance maximization.
Convergence, Learning systems, Logistics, Loss measurement,
Standards, Training
BibRef
Yang, Y.Z.[Ya-Zhou],
Loog, M.[Marco],
Single shot active learning using pseudo annotators,
PR(89), 2019, pp. 22-31.
Elsevier DOI
1902
Active learning, Pseudo annotators, Random labeling,
Single shot, Exploration and exploitation, Minimizing nearest neighbor distance
BibRef
Yang, Y.Z.[Ya-Zhou],
Loog, M.[Marco],
A benchmark and comparison of active learning for logistic regression,
PR(83), 2018, pp. 401-415.
Elsevier DOI
1808
Active learning, Logistic regression, Experimental design,
Benchmark, Preference maps
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,
Probability distribution, Risk, management
BibRef
Susnjak, T.[Teo],
Barczak, A.[Andre],
Reyes, N.[Napoleon],
Hawick, K.[Ken],
Coarse-to-fine multiclass learning and classification for time-critical
domains,
PRL(34), No. 8, June 2013, pp. 884-894.
Elsevier DOI
1305
BibRef
Earlier:
A New Ensemble-Based Cascaded Framework for Multiclass Training with
Simple Weak Learners,
CAIP11(I: 563-570).
Springer DOI
1109
Coarse-to-fine learning; Multiclass classification; Classifier
ensembles; Boosting; Classifier cascades; Training runtime constraints
BibRef
Li, N.[Nan],
Tsang, I.W.H.[Ivor W.H.],
Zhou, Z.H.[Zhi-Hua],
Efficient Optimization of Performance Measures by Classifier Adaptation,
PAMI(35), No. 6, June 2013, pp. 1370-1382.
IEEE DOI
1305
First train non-linear classifiers, then adapt by optimizing performance
measures.
BibRef
Mao, Q.[Qi],
Tsang, I.W.H.[Ivor Wai-Hung],
A Feature Selection Method for Multivariate Performance Measures,
PAMI(35), No. 9, 2013, pp. 2051-2063.
IEEE DOI
1307
Convergence. Optimize multi-variate measures, not just classification error.
BibRef
Bouges, P.[Pierre],
Chateau, T.[Thierry],
Blanc, C.[Christophe],
Loosli, G.[Gaëlle],
Handling missing weak classifiers in boosted cascade: application to
multiview and occluded face detection,
JIVP(2013), No. 1, 2013, pp. 55.
DOI Link
1311
BibRef
Earlier:
Using k-nearest neighbors to handle missing weak classifiers in a
boosted cascade,
ICPR12(1763-1766).
WWW Link.
1302
BibRef
Ludwig, O.,
Nunes, U.,
Ribeiro, B.,
Premebida, C.,
Improving the Generalization Capacity of Cascade Classifiers,
Cyber(43), No. 6, 2013, pp. 2135-2146.
IEEE DOI
1312
feature extraction
BibRef
Li, Y.L.[Ya-Li],
Wang, S.J.[Sheng-Jin],
Tian, Q.[Qi],
Ding, X.Q.[Xiao-Qing],
Learning Cascaded Shared-Boost Classifiers for Part-Based Object
Detection,
IP(23), No. 4, April 2014, pp. 1858-1871.
IEEE DOI
1404
image representation
BibRef
Li, Y.L.[Ya-Li],
Wang, S.J.[Sheng-Jin],
BooDet: Gradient Boosting Object Detection With Additive
Learning-Based Prediction Aggregation,
IP(31), 2022, pp. 2620-2632.
IEEE DOI
2204
Object detection, Feature extraction, Detectors, Convolution,
Location awareness, Boosting, Additives, Object detection,
additive learning
BibRef
Mansouri, J.[Jafar],
Khademi, M.[Morteza],
Tree Fusion Method for Semantic Concept Detection in Images,
IEICE(E97-D), No. 8, August 2014, pp. 2209-2211.
WWW Link.
1408
semantic concept detection.
BibRef
Hedhli, I.[Ihsen],
Moser, G.[Gabriele],
Zerubia, J.B.[Josiane B.],
Serpico, S.B.[Sebastiano B.],
A New Cascade Model for the Hierarchical Joint Classification of
Multitemporal and Multiresolution Remote Sensing Data,
GeoRS(54), No. 11, November 2016, pp. 6333-6348.
IEEE DOI
1610
BibRef
Earlier:
New cascade model for hierarchical joint classification of
multitemporal, multiresolution and multisensor remote sensing data,
ICIP14(5247-5251
IEEE DOI
1502
Data models
BibRef
Hanczar, B.[Blaise],
Bar-Hen, A.[Avner],
CASCARO: Cascade of classifiers for minimizing the cost of prediction,
PRL(149), 2021, pp. 37-43.
Elsevier DOI
2108
Classification with reject option, Cascade of classifiers
BibRef
Ouerghemmi, W.,
Le Bris, A.,
Chehata, N.,
Mallet, C.,
A Two-step Decision Fusion Strategy: Application to Hyperspectral And
Multispectral Images for Urban Classification,
Hannover17(167-174).
DOI Link
1805
BibRef
Rebuffi, S.A.[Sylvestre-Alvise],
Kolesnikov, A.[Alexander],
Sperl, G.[Georg],
Lampert, C.H.[Christoph H.],
iCaRL: Incremental Classifier and Representation Learning,
CVPR17(5533-5542)
IEEE DOI
1711
Classification algorithms, Feature extraction,
Memory management, Prototypes, Training, Training, data
BibRef
Qiu, Q.A.[Qi-Ang],
Sapiro, G.[Guillermo],
Learning Transformations,
ICIP14(4008-4012)
IEEE DOI
1502
Accuracy
BibRef
Torres-Pereira, E.[Eanes],
Martins-Gomes, H.[Herman],
Monteiro-Brito, A.E.[Andrey Elísio],
de Carvalho, J.M.[Joăo Marques],
Hybrid Parallel Cascade Classifier Training for Object Detection,
CIARP14(810-817).
Springer DOI
1411
BibRef
Chen, B.[Bo],
Perona, P.[Pietro],
Bourdev, L.[Lubomir],
Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Weiss, D.[David],
Sapp, B.[Benjamin],
Taskar, B.[Ben],
Dynamic Structured Model Selection,
ICCV13(2656-2663)
IEEE DOI
1403
pose estimation; structured prediction
BibRef
Marcialis, G.L.[Gian Luca],
Didaci, L.[Luca],
Roli, F.[Fabio],
Estimating the Serial Combination's Performance from That of Individual
Base Classifiers,
CIAP13(I:622-631).
Springer DOI
1311
BibRef
Sznitman, R.[Raphael],
Becker, C.[Carlos],
Fleuret, F.[Francois],
Fua, P.[Pascal],
Fast Object Detection with Entropy-Driven Evaluation,
CVPR13(3270-3277)
IEEE DOI
1309
Computer Vision. Speedup cascade style classifier combination.
BibRef
Yamasaki, T.[Toshihiko],
Chen, T.H.[Tsu-Han],
Confidence-assisted classification result refinement for object
recognition featuring TopN-Exemplar-SVM,
ICPR12(1783-1786).
WWW Link.
1302
Classifier cascade
BibRef
Chen, Y.T.[Yu-Tian],
Gelfand, A.[Andrew],
Fowlkes, C.C.[Charless C.],
Welling, M.[Max],
Integrating local classifiers through nonlinear dynamics on label
graphs with an application to image segmentation,
ICCV11(2635-2642).
IEEE DOI
1201
Combine locally trained models into globel model.
BibRef
Parvin, H.[Hamid],
Minaei-Bidgoli, B.[Behrouz],
Parvin, S.[Sajad],
A Scalable Heuristic Classifier for Huge Datasets:
A Theoretical Approach,
CIARP11(380-390).
Springer DOI
1111
BibRef
Parvin, H.[Hamid],
Minaei-Bidgoli, B.[Behrouz],
Parvin, S.[Sajad],
An Accumulative Points/Votes Based Approach for Feature Selection,
CIARP11(399-408).
Springer DOI
1111
BibRef
Jain, V.[Vidit],
Learned-Miller, E.G.[Erik G.],
Online domain adaptation of a pre-trained cascade of classifiers,
CVPR11(577-584).
IEEE DOI
1106
BibRef
Preet, P.,
Chowdhury, P.R.,
Malik, G.S.,
Correlation based object-specific attentional mechanism for target
localization in high resolution satellite images,
NCVPRIPG13(1-4)
IEEE DOI
1408
geophysical image processing
BibRef
Mangai, U.G.,
Samanta, S.,
Das, S.,
Chowdhury, P.R.,
Varghese, K.,
Kalra, M.,
A Hierarchical Multi-classifier Framework for Landform Segmentation
Using Multi-spectral Satellite Images: A Case Study over the Indian
Subcontinent,
PSIVT10(306-313).
IEEE DOI
1011
BibRef
Wang, P.[Peng],
Shen, C.H.[Chun-Hua],
Zheng, H.[Hong],
Ren, Z.[Zhang],
Training a multi-exit cascade with linear asymmetric classification for
efficient object detection,
ICIP10(61-64).
IEEE DOI
1009
BibRef
Day, M.[Matthew],
Robinson, J.A.[John A.],
Constructing efficient cascade classifiers for object detection,
ICIP10(3781-3784).
IEEE DOI
1009
BibRef
Cordella, L.P.[Luigi P.],
de Stefano, C.[Claudio],
Fontanella, F.[Francesco],
Marrocco, C.[Cristina],
di Freca, A.S.[Alessandra Scotto],
Combining Single Class Features for Improving Performance of a Two
Stage Classifier,
ICPR10(4352-4355).
IEEE DOI
1008
BibRef
Szczot, M.[Magdalena],
Forster, J.[Julian],
Lohlein, O.[Otto],
Palm, G.[Gunther],
Package Boosting for Readaption of Cascaded Classifiers,
ICPR10(552-555).
IEEE DOI
1008
BibRef
Zhang, X.Q.[Xu-Qing],
Wu, F.[Fei],
Zhuang, Y.T.[Yue-Ting],
Clustering by evidence accumulation on affinity propagation,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Kukenys, I.[Ignas],
Browne, W.N.[Will N.],
Zhang, M.J.[Meng-Jie],
Transparent, Online Image Pattern Classification Using a Learning
Classifier System,
EvoIASP11(183-193).
Springer DOI
1104
BibRef
Kukenys, I.[Ignas],
McCane, B.[Brendan],
Neumegen, T.[Tim],
Training Support Vector Machines on Large Sets of Image Data,
ACCV09(III: 331-340).
Springer DOI
0909
BibRef
Kukenys, I.[Ignas],
McCane, B.[Brendan],
Classifier cascades for support vector machines,
IVCNZ08(1-6).
IEEE DOI
0811
BibRef
Mirzaei, A.[Abdolreza],
Rahmati, M.[Mohammad],
Combining hierarchical clusterings using min-transitive closure,
ICPR08(1-4).
IEEE DOI
0812
BibRef
El-Sherif, E.[Ezzat],
Abdelazeem, S.[Sherif],
El-Yazeed, M.F.A.[M. Fathy Abu],
Automatic generation of optimum classification cascades,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Concepción Morales, E.R.[Eduardo R.],
Yurramendi Mendizabal, Y.[Yosu],
Building and Assessing a Constrained Clustering Hierarchical Algorithm,
CIARP08(211-218).
Springer DOI
0809
BibRef
Ranzato, M.[Marc'Aurelio],
Hinton, G.E.[Geoffrey E.],
Modeling pixel means and covariances using factorized third-order
boltzmann machines,
CVPR10(2551-2558).
IEEE DOI
1006
BibRef
Kavukcuoglu, K.[Koray],
Ranzato, M.[Marc'Aurelio],
Fergus, R.[Rob],
Le Cun, Y.L.[Yann L.],
Learning invariant features through topographic filter maps,
CVPR09(1605-1612).
IEEE DOI
0906
BibRef
Boureau, Y.L.[Y-Lan],
Le Roux, N.[Nicolas],
Bach, F.[Francis],
Ponce, J.[Jean],
Le Cun, Y.L.[Yann L.],
Ask the locals: Multi-way local pooling for image recognition,
ICCV11(2651-2658).
IEEE DOI
1201
Pooling feature vectors over neighborhoods is not local in feature
space. Apply to feature space also.
BibRef
Boureau, Y.L.[Y-Lan],
Bach, F.[Francis],
Le Cun, Y.L.[Yann L.],
Ponce, J.[Jean],
Learning mid-level features for recognition,
CVPR10(2559-2566).
IEEE DOI
1006
BibRef
Ranzato, M.[Marc'Aurelio],
Huang, F.J.[Fu Jie],
Boureau, Y.L.[Y-Lan],
Le Cun, Y.L.[Yann L.],
Unsupervised Learning of Invariant Feature Hierarchies with
Applications to Object Recognition,
CVPR07(1-8).
IEEE DOI
0706
Hierarchical representation. Learn on features, then on patches of features
from first level.
BibRef
Dundar, M.M.[M. Murat],
Bi, J.B.[Jin-Bo],
Joint Optimization of Cascaded Classifiers for Computer Aided Detection,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Chen, H.X.[Hai-Xia],
Yuan, S.[Senmiao],
Jiang, K.[Kai],
Adaptive Classifier Selection Based on Two Level Hypothesis Tests for
Incremental Learning,
SSPR06(687-695).
Springer DOI
0608
BibRef
Luo, H.T.[Hui-Tao],
Optimization Design of Cascaded Classifiers,
CVPR05(I: 480-485).
IEEE DOI
0507
BibRef
Hamamura, T.,
Mizutani, H.,
Irie, B.,
A multiclass classification method based on multiple pairwise
classifiers,
ICDAR03(809-813).
IEEE DOI
0311
BibRef
Heiseleyz, B.[Bernd],
Serrey, T.[Thomas],
Mukherjeey, S.[Sayan],
Poggio, T.[Tomaso],
Feature Reduction and Hierarchy of Classifiers for Fast Object
Detection in Video Images,
CVPR01(II:18-24).
IEEE DOI
0110
Speed up object detection using SVM classifiers.
Hierarchy with many selected first, then more accurate.
BibRef
Chou, Y.Y.,
Shapiro, L.G.,
A Hierarchical Multiple Classifier Learning Algorithm,
ICPR00(Vol II: 152-155).
IEEE DOI
0009
BibRef
Sun, F.,
Omachi, S.,
Kato, N.,
Aso, H.,
Kono, S.,
Takagi, T.,
Two-stage Computational Cost Reduction Algorithm Based on Mahalanobis
Distance Approximations,
ICPR00(Vol II: 696-699).
IEEE DOI
0009
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Classifier Combination, Evaluation, Overview, Appliction Specific .