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0309
Experts can lead to biases in fusion rules if training of experts and
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Fraud detection; Telecommunications; User modelling; Data mining;
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0409
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IEEE Abstract.
0408
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Mixed Group Ranks: Preference and Confidence in Classifier Combination,
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0407
Analyze rules for combining when a large number of classes (biometrics).
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0411
Build tree classifier, cluster them.
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0611
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0509
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Feasible Adaptation Criteria for Hybrid Wavelet:
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0311
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0509
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0609
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0311
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0510
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Multiple neural networks and weighted voting,
ICPR92(II:29-32).
IEEE DOI Link
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BibRef
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IEEE DOI Link
0512
First uniform representation for multiple classifiers.
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ICPR04(I: 272-275).
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0409
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CIARP11(320-330).
Springer DOI Link
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PR(39), No. 3, March 2006, pp. 488-490.
WWW Version.
0601
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Ensemblator: An ensemble of classifiers for reliable classification of
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PRL(28), No. 5, 1 April 2007, pp. 622-630.
WWW Version.
0703
Ensemble of classifiers; Machine learning; Bioinformatics
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Laaksonen, J.T.[Jorma T.],
Using diversity of errors for selecting members of a committee
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PR(39), No. 4, April 2006, pp. 608-623.
WWW Version. Classifier combining; Committee classifier; Diversity; Diversity of errors
0604
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Girdziusas, R.,
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0605
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A cooperative constructive method for neural networks for pattern
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PR(40), No. 1, January 2007, pp. 80-98.
WWW Version.
0611
Constructive algorithms; Pattern classification; Evolutionary computation;
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PR(42), No. 9, September 2009, pp. 1742-1760.
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0905
Classification; Ensembles of classifiers; Boosting; Supervised projections
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Lu, Z.W.[Zhi-Wu],
A regularized minimum cross-entropy algorithm on mixtures of experts
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PRL(27), No. 9, July 2006, pp. 947-955.
WWW Version. Regularization theory; Model selection; Time series prediction; Curve detection
0605
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Goodband, J.H.,
Haas, O.C.L.,
Mills, J.A.,
A mixture of experts committee machine to design compensators for
intensity modulated radiation therapy,
PR(39), No. 9, September 2006, pp. 1704-1714.
WWW Version.
0606
Committee machines; Neural networks; Fuzzy C-means; Compensators;
Radiation therapy
BibRef
Wanas, N.M.[Nayer M.],
Dara, R.A.[Rozita A.],
Kamel, M.S.[Mohamed S.],
Adaptive fusion and co-operative training for classifier ensembles,
PR(39), No. 9, September 2006, pp. 1781-1794.
WWW Version.
0606
Decision fusion; Co-operative training; Combining architecture
BibRef
Dara, R.A.[Rozita A.],
Kamel, M.S.[Mohamed S.],
Wanas, N.M.[Nayer M.],
Data dependency in multiple classifier systems,
PR(42), No. 7, July 2009, pp. 1260-1273.
Elsevier DOI Link
WWW Version.
0903
Multiple classifier systems; Data dependency; Aggregation methods;
Stability; Class imbalance
BibRef
Chen, L.[Lei],
Kamel, M.S.[Mohamed S.],
A generalized adaptive ensemble generation and aggregation approach for
multiple classifier systems,
PR(42), No. 5, May 2009, pp. 629-644.
Elsevier DOI Link
WWW Version.
0902
Pattern recognition; Data mining; Classification; Classifier
combination; Multiple classifier systems
BibRef
Hu, T.M.[Tian-Ming],
Yu, Y.[Ying],
Xiong, J.Z.[Jin-Zhi],
Sung, S.Y.[Sam Yuan],
Maximum likelihood combination of multiple clusterings,
PRL(27), No. 13, 1 October 2006, pp. 1457-1464.
WWW Version.
0606
Consensus clustering; Centroid clustering; Markov random field;
Metric distance function
BibRef
Rooney, N.[Niall],
Patterson, D.[David],
A weighted combination of stacking and dynamic integration,
PR(40), No. 4, April 2007, pp. 1385-1388.
WWW Version.
0701
Ensemble learning; Stacking; Regression
BibRef
Rooney, N.[Niall],
Patterson, D.[David],
Nugent, C.[Chris],
Non-strict heterogeneous Stacking,
PRL(28), No. 9, 1 July 2007, pp. 1050-1061.
WWW Version.
0704
Ensemble learning; Meta learning; Regression
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.
WWW Version.
0704
BibRef
Earlier: A1, A2, A3, Only:
A New Objective Function for Ensemble Selection in Random Subspaces,
ICPR06(IV: 185-188).
WWW Version.
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.
WWW Version.
0711
BibRef
Earlier:
K-Nearest Oracle for Dynamic Ensemble Selection,
ICDAR07(422-426).
IEEE DOI Link
0709
Oracle; Combining classifiers; Classifier selection; Ensemble selection;
Pattern recognition; Majority voting; Ensemble of learning machines
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 Link
0812
BibRef
Hu, Q.H.[Qing-Hua],
Yu, D.R.[Da-Ren],
Xie, Z.X.[Zong-Xia],
Li, X.D.[Xiao-Dong],
EROS: Ensemble rough subspaces,
PR(40), No. 12, December 2007, pp. 3728-3739.
WWW Version.
0709
Ensemble systems initially improve performance as more added,
the decrease.
Attribute reduction; Ensemble learning; Multiple classifier system;
Rough set; Selective ensemble
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Reformat, M.[Marek],
Aggregation of classifiers based on image transformations in biometric
face recognition,
MVA(19), No. 2, March 2008, pp. 125-140.
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0802
BibRef
Kim, Y.W.[Young-Won],
Oh, I.S.[Il-Seok],
Classifier ensemble selection using hybrid genetic algorithms,
PRL(29), No. 6, 15 April 2008, pp. 796-802.
WWW Version.
0803
Multiple classifier combination; Ensemble selection; Genetic algorithm;
Local search operation
See also Local search-embedded genetic algorithms for feature selection.
BibRef
Bogdanov, A.V.,
Neuroinspired Architecture for Robust Classifier Fusion of Multisensor
Imagery,
GeoRS(46), No. 5, May 2008, pp. 1467-1487.
IEEE DOI Link
0804
BibRef
Asdornwised, W.[Widhyakorn],
Jitapunkul, S.[Somchai],
Multiple Description Pattern Analysis: Robustness to Misclassification
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IEICE(E88-D), No. 10, October 2005, pp. 2296-2307.
WWW Version.
0510
Apply to ATR task.
BibRef
Zhu, X.Q.[Xing-Quan],
Yang, Y.[Ying],
A lazy bagging approach to classification,
PR(41), No. 10, October 2008, pp. 2980-2992.
WWW Version.
0808
Classification; Classifier ensemble; Bagging; Lazy learning
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.
WWW Version.
0808
Overproduce-and-choose strategy; Dynamic classifier selection;
Optimization; Measures of confidence
BibRef
Toh, K.A.[Kar-Ann],
Kim, J.H.[Jai-Hie],
Lee, S.Y.[Sang-Youn],
Maximizing area under ROC curve for biometric scores fusion,
PR(41), No. 11, November 2008, pp. 3373-3392.
WWW Version.
0808
Receiver operating characteristics; Biometrics; Decision fusion;
Machine learning; Pattern classification
BibRef
Tumer, K.[Kagan],
Agogino, A.K.[Adrian K.],
Ensemble clustering with voting active clusters,
PRL(29), No. 14, October 2008, pp. 1947-1953.
WWW Version.
0804
Cluster ensembles; Consensus clustering; Distributed clustering;
Adaptive clustering
BibRef
Carneiro, G.[Gustavo],
Vasconcelos, N.[Nuno],
Minimum Bayes error features for visual recognition,
IVC(27), No. 1-2, January 2009, pp. 131-140.
WWW Version.
0811
BibRef
Earlier:
Minimum Bayes Error Features for Visual Recognition by Sequential
Feature Selection and Extraction,
CRV05(253-260).
IEEE DOI Link
0505
Visual recognition; Feature selection; Feature extraction;
Minimum Bayes error; Mixture models; Face recognition;
Texture recognition; Object recognition
BibRef
Martínez-Muñoz, G.[Gonzalo],
Hernández-Lobato, D.[Daniel],
Suárez, A.[Alberto],
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation,
PAMI(31), No. 2, February 2009, pp. 245-259.
IEEE DOI Link
0901
reduce size of ensembles for classifiers.
BibRef
Hernández-Lobato, D.[Daniel],
Martínez-Muñoz, G.[Gonzalo],
Suárez, A.[Alberto],
Statistical Instance-Based Pruning in Ensembles of Independent
Classifiers,
PAMI(31), No. 2, February 2009, pp. 364-369.
IEEE DOI Link
0901
BibRef
Bacauskiene, M.[Marija],
Verikas, A.[Antanas],
Gelzinis, A.[Adas],
Valincius, D.,
A feature selection technique for generation of classification
committees and its application to categorization of laryngeal images,
PR(42), No. 5, May 2009, pp. 645-654.
Elsevier DOI Link
WWW Version.
0902
Feature selection; Variable selection; Classification committee;
Genetic search; Support vector machine; Laryngeal image
BibRef
Wang, X.[Xi],
Yang, C.[Chunyu],
Zhou, J.[Jie],
Clustering aggregation by probability accumulation,
PR(42), No. 5, May 2009, pp. 668-675.
Elsevier DOI Link
WWW Version.
0902
BibRef
Earlier:
Spectral aggregation for clustering ensemble,
ICPR08(1-4).
IEEE DOI Link
0812
Clustering aggregation; Evidence accumulation; Probability accumulation
BibRef
Chaudhuri, P.[Probal],
Ghosh, A.K.[Anil K.],
Oja, H.[Hannu],
Classification Based on Hybridization of Parametric and Nonparametric
Classifiers,
PAMI(31), No. 7, July 2009, pp. 1153-1164.
IEEE DOI Link
0905
Deal with problems where parametric classifier assumptions fail, but a small
number of training samples cause nonparameteric failures. Combine
strengths of each.
BibRef
Terrades, O.R.[Oriol Ramos],
Valveny, E.[Ernest],
Tabbone, S.A.[Salvatore A.],
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework,
PAMI(31), No. 9, September 2009, pp. 1630-1644.
IEEE DOI Link
0907
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 Link
WWW Version.
0909
Learning by pairwise comparison; Label ranking; Aggregation
strategies; Classifier combination; Weighted voting; MAP prediction
BibRef
Martinez-Munoz, G.[Gonzalo],
Suarez, A.[Alberto],
Out-of-bag estimation of the optimal sample size in bagging,
PR(43), No. 1, January 2010, pp. 143-152,.
Elsevier DOI Link
WWW Version.
0909
Bagging; Subagging; Bootstrap sampling; Subsampling; Optimal sampling
ratio; Ensembles of classifiers; Decision trees
BibRef
Tzeng, Y.C.[Yu-Chang],
Fan, K.T.[Kou-Tai],
Chen, K.S.[Kun-Shan],
An Adaptive Thresholding Multiple Classifiers System for Remote Sensing
Image Classification,
PhEngRS(75), No. 6, June 2009, pp. 679-688.
WWW Version.
0910
Bagging and/or Boosting Weighted Multiple Classifiers Systems with an
Adaptive Thresholding for Remote Sensing Image Classification
BibRef
Couturier, S.[Stéphane],
Mas, J.F.[Jean-François],
Mountrakis, G.[Giorgos],
Watts, R.[Raymond],
Luo, L.[Lori],
Wang, J.[Jida],
Developing Collaborative Classifiers using an Expert-based Model,
PhEngRS(75), No. 7, July 2009, pp. 831-844.
WWW Version.
0910
A novel framework for integrating classifiers of variable complexity
as applied to specific portions of the input space.
BibRef
Sun, S.L.[Shi-Liang],
Local within-class accuracies for weighting individual outputs in
multiple classifier systems,
PRL(31), No. 2, 15 January 2010, pp. 119-124.
Elsevier DOI Link
WWW Version.
1001
Distance metric learning; Local within-class accuracy; Majority
voting; Multiple classifier system; Random subspace
BibRef
Correa-Morris, J.[Jyrko],
Espinosa-Isidron, D.L.[Dustin L.],
Alvarez-Nadiozhin, D.R.[Denis R.],
An incremental nested partition method for data clustering,
PR(43), No. 7, July 2010, pp. 2439-2455.
Elsevier DOI Link
WWW Version.
1003
Nested partition; Data clustering; Incremental clustering
BibRef
Correa-Morris, J.[Jyrko],
Ruiz-Shulcloper, J.[Jose],
Espinosa-Isidron, D.L.[Dustin L.],
Pons-Porrata, A.[Aurora],
Incremental nested partition method,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Vega-Pons, S.[Sandro],
Ruiz-Shulcloper, J.[José],
Guerra-Gandón, A.[Alejandro],
Weighted association based methods for the combination of heterogeneous
partitions,
PRL(32), No. 16, 1 December 2011, pp. 2163-2170.
Elsevier DOI Link
WWW Version.
1112
Clustering ensemble; Similarity measure; Evidence accumulation; Object
representation; Kernel function
BibRef
Vega-Pons, S.[Sandro],
Ruiz-Shulcloper, J.[José],
Clustering Ensemble Method for Heterogeneous Partitions,
CIARP09(481-488).
Springer DOI Link
0911
BibRef
Vega-Pons, S.[Sandro],
Correa-Morris, J.[Jyrko],
Ruiz-Shulcloper, J.[José],
Weighted partition consensus via kernels,
PR(43), No. 8, August 2010, pp. 2712-2724.
Elsevier DOI Link
WWW Version.
1006
BibRef
Earlier:
Weighted Cluster Ensemble Using a Kernel Consensus Function,
CIARP08(195-202).
Springer DOI Link
0809
Cluster ensemble; Kernel function; Similarity measure; Clustering
validity index; Consensus partition
BibRef
Khreich, W.[Wael],
Granger, E.[Eric],
Miri, A.[Ali],
Sabourin, R.[Robert],
Iterative Boolean combination of classifiers in the ROC space: An
application to anomaly detection with HMMs,
PR(43), No. 8, August 2010, pp. 2732-2752.
Elsevier DOI Link
WWW Version.
1006
BibRef
And:
Boolean Combination of Classifiers in the ROC Space,
ICPR10(4299-4303).
IEEE DOI Link
1008
Receiver operating characteristics; Combination of classifiers;
Limited and imbalanced data; Hidden Markov models; Anomaly detection;
Computer and network security
BibRef
Khreich, W.[Wael],
Granger, E.[Eric],
Miri, A.[Ali],
Sabourin, R.[Robert],
Adaptive ROC-based ensembles of HMMs applied to anomaly detection,
PR(45), No. 1, January 2012, pp. 208-230.
Elsevier DOI Link
WWW Version.
1109
Classification; Multi-classifier systems; Incremental learning;
Adaptive systems; ROC; Information fusion; Hidden Markov models;
Anomaly detection; Computer and network security
BibRef
Meynet, J.[Julien],
Thiran, J.P.[Jean-Philippe],
Information theoretic combination of pattern classifiers,
PR(43), No. 10, October 2010, pp. 3412-3421.
Elsevier DOI Link
WWW Version.
1007
Machine learning; Pattern recognition; Classifier combination;
Information theory; Mutual information; Diversity
BibRef
Zhang, L.[Li],
Zhou, W.D.[Wei-Da],
Sparse ensembles using weighted combination methods based on linear
programming,
PR(44), No. 1, January 2011, pp. 97-106.
Elsevier DOI Link
WWW Version.
1003
Classifier ensemble; Linear weighted combination; Linear programming;
Sparse ensembles; k nearest neighbor
BibRef
Montalvao, J.[Jugurta],
Canuto, J.[Janio],
Clustering ensembles and space discretization:
A new regard toward diversity and consensus,
PRL(31), No. 15, 1 November 2010, pp. 2415-2424.
Elsevier DOI Link
WWW Version.
1003
Clustering ensembles; Weak partitions; ANMI criterion; Binary morphology
BibRef
Wang, L.[Liang],
Leckie, C.[Christopher],
Kotagiri, R.[Ramamohanarao],
Bezdek, J.[James],
Approximate pairwise clustering for large data sets via sampling plus
extension,
PR(44), No. 2, February 2011, pp. 222-235.
Elsevier DOI Link
WWW Version.
1011
Pairwise data; Selective sampling; Spectral clustering; Graph
embedding; Out-of-sample extension
BibRef
Wang, L.[Liang],
Leckie, C.[Christopher],
Kotagiri, R.[Ramamohanarao],
Combining Real and Virtual Graphs to Enhance Data Clustering,
ICPR10(790-793).
IEEE DOI Link
1008
BibRef
Moshtaghi, M.[Masud],
Rajasegarar, S.[Sutharshan],
Leckie, C.[Christopher],
Karunasekera, S.[Shanika],
An efficient hyperellipsoidal clustering algorithm for
resource-constrained environments,
PR(44), No. 9, September 2011, pp. 2197-2209.
Elsevier DOI Link
WWW Version.
1106
HyCARCE; Data clustering; Hyperellipsoidal clustering; Wireless sensor
networks; Low computational cost clustering algorithm
BibRef
Rokach, L.[Lior],
Pattern Classification Using Ensemble Methods,
World ScientificSingapore, 2009
ISBN: 978-981-4271-06-6
HTML Version.
Survay, Pattern Classification. To purchase this book look here
Describe classical methods and new approaches to help determine which
to use for particular problems.
BibRef
0900
Mimaroglu, S.[Selim],
Erdil, E.[Ertunc],
Combining multiple clusterings using similarity graph,
PR(44), No. 3, March 2011, pp. 694-703.
Elsevier DOI Link
WWW Version.
1011
Clustering; Combining clustering partitions; Cluster ensemble;
Evidence accumulation; Robust clustering; Mutual information
BibRef
Mimaroglu, S.[Selim],
Aksehirli, E.[Emin],
Improving DBSCAN's execution time by using a pruning technique on bit
vectors,
PRL(32), No. 13, 1 October 2011, pp. 1572-1580.
Elsevier DOI Link
WWW Version.
1109
Clustering; DBSCAN; Binary methods; Pruning
BibRef
Christou, I.T.[Ioannis T.],
Coordination of Cluster Ensembles via Exact Methods,
PAMI(33), No. 2, February 2011, pp. 279-293.
IEEE DOI Link
1101
Optimization cluster combinations.
BibRef
Guru, D.S.,
Suraj, M.G.,
Manjunath, S.,
Fusion of covariance matrices of PCA and FLD,
PRL(32), No. 3, 1 February 2011, pp. 432-440.
Elsevier DOI Link
WWW Version.
1101
Classifier fusion; Appearance based approach; Covariance matrix; Data
clustering; Video retrieval
See also Archival and retrieval of symbolic images: An invariant scheme based on triangular spatial relationship.
BibRef
Gurrutxaga, I.[Ibai],
Muguerza, J.[Javier],
Arbelaitz, O.[Olatz],
Perez, J.M.[Jesus M.],
Martin, J.I.[Jose I.],
Towards a standard methodology to evaluate internal cluster validity
indices,
PRL(32), No. 3, 1 February 2011, pp. 505-515.
Elsevier DOI Link
WWW Version.
1101
Cluster validation; Cluster validity index; Unsupervised learning
BibRef
Mao, S.[Shasha],
Jiao, L.C.,
Xiong, L.[Lin],
Gou, S.P.[Shui-Ping],
Greedy optimization classifiers ensemble based on diversity,
PR(44), No. 6, June 2011, pp. 1245-1261.
Elsevier DOI Link
WWW Version.
1102
Diversity; Matching pursuit; Greedy optimization; Residual; Selective
ensemble; Kappa-error diagram
BibRef
Hernandez-Lobato, D.[Daniel],
Martinez-Munoz, G.[Gonzalo],
Suarez, A.[Alberto],
Inference on the prediction of ensembles of infinite size,
PR(44), No. 7, July 2011, pp. 1426-1434.
Elsevier DOI Link
WWW Version.
1103
Classification ensembles; Classification trees; Bayesian inference;
Infinite ensembles
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 Link
WWW Version.
1104
Survey, Ensemble Clustering. Multi-classification; Pairwise learning; One-vs-one; One-vs-all;
Decomposition strategies; Ensembles
BibRef
Jia, J.H.[Jian-Hua],
Xiao, X.[Xuan],
Liu, B.[Bingxiang],
Jiao, L.C.[Li-Cheng],
Bagging-based spectral clustering ensemble selection,
PRL(32), No. 10, 15 July 2011, pp. 1456-1467.
Elsevier DOI Link
WWW Version.
1106
Spectral clustering; Selective clustering ensembles; Bagging;
Normalized mutual information (NMI); Adjusted rand index (ARI)
BibRef
Irle, A.[Albrecht],
Kauschke, J.[Jonas],
On Kleinberg's Stochastic Discrimination Procedure,
PAMI(33), No. 7, July 2011, pp. 1482-1486.
IEEE DOI Link
1106
See also On the Algorithmic Implementation of Stochastic Discrimination.
BibRef
Ricamato, M.T.[Maria Teresa],
Tortorella, F.[Francesco],
Partial AUC maximization in a linear combination of dichotomizers,
PR(44), No. 10-11, October-November 2011, pp. 2669-2677.
Elsevier DOI Link
WWW Version.
1101
BibRef
Earlier:
AUC-based Combination of Dichotomizers: Is Whole Maximization also
Effective for Partial Maximization?,
ICPR10(73-76).
IEEE DOI Link
1008
Combination of classifiers; ROC analysis; Partial AUC
BibRef
Murino, L.,
Angelini, C.,
de Feis, I.,
Raiconi, G.,
Tagliaferri, R.,
Beyond classical consensus clustering:
The least squares approach to multiple solutions,
PRL(32), No. 12, 1 September 2011, pp. 1604-1612.
Elsevier DOI Link
WWW Version.
1108
Clustering; Least-squares consensus; Data visualization
BibRef
Asman, A.J.,
Landman, B.A.,
Robust Statistical Label Fusion Through Consensus Level, Labeler
Accuracy, and Truth Estimation (COLLATE),
MedImg(30), No. 10, October 2011, pp. 1779-1794.
IEEE DOI Link
1110
BibRef
Geng, B.[Bo],
Tao, D.C.[Da-Cheng],
Xu, C.[Chao],
DAML: Domain Adaptation Metric Learning,
IP(20), No. 10, October 2011, pp. 2980-2989.
IEEE DOI Link
1110
BibRef
Geng, B.[Bo],
Xu, C.[Chao],
Tao, D.C.[Da-Cheng],
Yang, L.J.[Lin-Jun],
Hua, X.S.[Xian-Sheng],
Ensemble manifold regularization,
CVPR09(2396-2402).
IEEE DOI Link
0906
approximate the intrinsic manifold by combining several initial guesses
BibRef
Iam-On, N.[Natthakan],
Boongoen, T.[Tossapon],
Garrett, S.[Simon],
Price, C.[Chris],
A Link-Based Approach to the Cluster Ensemble Problem,
PAMI(33), No. 12, December 2011, pp. 2396-2409.
IEEE DOI Link
1110
Also use similarity between clusters in consensus.
BibRef
Madjarov, G.[Gjorgji],
Gjorgjevikj, D.[Dejan],
Džeroski, S.[Sašo],
Two stage architecture for multi-label learning,
PR(45), No. 3, March 2012, pp. 1019-1034.
Elsevier DOI Link
WWW Version.
1111
BibRef
And:
Dual Layer Voting Method for Efficient Multi-label Classification,
IbPRIA11(232-239).
Springer DOI Link
1106
Multi-label learning; Multi-label ranking; Multi-label classification;
Two stage architecture; Classifier chain
BibRef
Lu, Z.W.[Zhi-Wu],
Peng, Y.X.[Yu-Xin],
Ip, H.H.S.[Horace H.S.],
Combining multiple clusterings using fast simulated annealing,
PRL(32), No. 15, 1 November 2011, pp. 1956-1961.
Elsevier DOI Link
WWW Version.
1112
Clustering ensemble; Comparing clusterings; Simulated annealing
BibRef
Tian, J.[Jin],
Li, M.Q.A.[Min-Qi-Ang],
Chen, F.[Fuzan],
Kou, J.[Jisong],
Coevolutionary learning of neural network ensemble for complex
classification tasks,
PR(45), No. 4, April 2012, pp. 1373-1385.
Elsevier DOI Link
WWW Version.
1112
Ensemble learning; Neural network; Coevolutionary algorithm; Classification
BibRef
Meo, R.[Rosa],
Bachar, D.[Dipankar],
Ienco, D.[Dino],
LODE: A distance-based classifier built on ensembles of positive and
negative observations,
PR(45), No. 4, April 2012, pp. 1409-1425.
Elsevier DOI Link
WWW Version.
1112
Data mining; Supervised learning; Concept learning; Associative classifier
BibRef
Pang, Y.W.[Yan-Wei],
Ma, Z.[Zhao],
Yuan, Y.[Yuan],
Li, X.L.[Xue-Long],
Wang, K.Q.[Kong-Qiao],
Multimodal learning for multi-label image classification,
ICIP11(1797-1800).
IEEE DOI Link
1201
BibRef
Ponti, M.P.[Moacir P.],
Papa, J.P.[João Paulo],
Levada, A.L.M.[Alexandre L. M.],
A Markov Random Field Model for Combining Optimum-Path Forest
Classifiers Using Decision Graphs and Game Strategy Approach,
CIARP11(581-590).
Springer DOI Link
1111
BibRef
Ñanculef, R.[Ricardo],
López, E.[Erick],
Allende, H.[Héctor],
Allende-Cid, H.[Héctor],
An Ensemble Method for Incremental Classification in Stationary and
Non-stationary Environments,
CIARP11(541-548).
Springer DOI Link
1111
BibRef
Rajasekhara, P.,
Pujari, A.K.[Arun K.],
A new clusterwise similarity for partitions based on quantitative
disagreement,
ICCVGIP10(117-123).
WWW Version.
1111
Get consnesus clustering.
BibRef
Cai, X.[Xiao],
Nie, F.P.[Fei-Ping],
Huang, H.[Heng],
Kamangar, F.[Farhad],
Heterogeneous image feature integration via multi-modal spectral
clustering,
CVPR11(1977-1984).
IEEE DOI Link
1106
To combine the various features (SIFT, HOG, GIST, LBP, CENTRIST) using MMSC.
BibRef
Ota, T.[Takahiro],
Wada, T.[Toshikazu],
Nakamura, T.[Takayuki],
Classifier Acceleration by Imitation,
ACCV10(IV: 653-664).
Springer DOI Link
1011
Classifier Molding. Imitate arbirtary classifiers by linear regression
trees.
BibRef
Xue, X.Y.[Xiang-Yang],
Luo, H.Z.[Hang-Zai],
Fan, J.P.[Jian-Ping],
Structured max-margin learning for multi-label image annotation,
CIVR10(82-88).
WWW Version.
1007
Multiple classifiers.
BibRef
Vega-Pons, S.[Sandro],
Ruiz-Shulcloper, J.[José],
Partition Selection Approach for Hierarchical Clustering Based on
Clustering Ensemble,
CIARP10(525-532).
Springer DOI Link
1011
BibRef
Wandekokem, E.D.[Estefhan Dazzi],
Varejão, F.M.[Flávio M.],
Rauber, T.W.[Thomas W.],
An Overproduce-and-Choose Strategy to Create Classifier Ensembles with
Tuned SVM Parameters Applied to Real-World Fault Diagnosis,
CIARP10(500-508).
Springer DOI Link
1011
BibRef
Yuksel, S.E.[Seniha Esen],
Gader, P.D.[Paul D.],
Variational Mixture of Experts for Classification with Applications to
Landmine Detection,
ICPR10(2981-2984).
IEEE DOI Link
1008
BibRef
Armano, G.[Giuliano],
Hatami, N.[Nima],
Random Prototype-based Oracle for Selection-fusion Ensembles,
ICPR10(77-80).
IEEE DOI Link
1008
BibRef
Zhang, S.H.[Shao-Hong],
Wong, H.S.[Hau-San],
ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles,
ICPR10(778-781).
IEEE DOI Link
1008
BibRef
Mirzaei, H.[Hamidreza],
A Novel Multi-view Agglomerative Clustering Algorithm Based on Ensemble
of Partitions on Different Views,
ICPR10(1007-1010).
IEEE DOI Link
1008
BibRef
Abdala, D.D.[Daniel Duarte],
Wattuya, P.[Pakaket],
Jiang, X.Y.[Xiao-Yi],
Ensemble Clustering via Random Walker Consensus Strategy,
ICPR10(1433-1436).
IEEE DOI Link
1008
BibRef
Senko, O.V.[Oleg V.],
Kuznetsova, A.V.[Anna V.],
Pattern Recognition Method Using Ensembles of Regularities Found by
Optimal Partitioning,
ICPR10(2957-2960).
IEEE DOI Link
1008
BibRef
Erdogan, H.[Hakan],
Sen, M.U.[Mehmet Umut],
A Unifying Framework for Learning the Linear Combiners for Classifier
Ensembles,
ICPR10(2985-2988).
IEEE DOI Link
1008
BibRef
Kim, T.K.[Tae-Kyun],
Woodley, T.[Thomas],
Stenger, B.[Bjorn],
Stenger, B.[Björn],
Cipolla, R.[Roberto],
Online multiple classifier boosting for object tracking,
OLCV10(1-6).
IEEE DOI Link
1006
BibRef
Siddiquie, B.[Behjat],
Vitaladevuni, S.N.[Shiv N.],
Davis, L.S.[Larry S.],
Combining multiple kernels for efficient image classification,
WACV09(1-8).
IEEE DOI Link
0912
Multiple features for recognition, combine classifications.
BibRef
Takahashi, T.[Tetsuji],
Kudo, M.[Mineichi],
Nakamura, A.[Atsuyoshi],
Classifier Selection in a Family of Polyhedron Classifiers,
CIARP09(441-448).
Springer DOI Link
0911
BibRef
Duin, R.P.W.[Robert P. W.],
Pekkalska, E.[Elzbieta],
The Dissimilarity Representation for Structural Pattern Recognition,
CIARP11(1-24).
Springer DOI Link
1111
BibRef
Kim, S.W.[Sang-Woon],
Duin, R.P.W.[Robert P.W.],
Dissimilarity-Based Classifications in Eigenspaces,
CIARP11(425-432).
Springer DOI Link
1111
BibRef
Earlier:
On Improving Dissimilarity-Based Classifications Using a Statistical
Similarity Measure,
CIARP10(418-425).
Springer DOI Link
1011
BibRef
Earlier:
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based
Classifiers,
CIARP09(425-432).
Springer DOI Link
0911
BibRef
Liu, Y.[Yu],
Xu, X.J.[Xiu-Juan],
Wang, C.[Chunyu],
Simple Ensemble of Extreme Learning Machine,
CISP09(1-5).
IEEE DOI Link
0910
BibRef
Li, J.J.[Jian-Jun],
Wei, Z.H.[Zhi-Hui],
Zhang, Z.J.[Zheng-Jun],
Xie, J.C.[Jian-Chun],
Edge Detection Method Based on Multi-Expert Information Fusion,
CISP09(1-4).
IEEE DOI Link
0910
BibRef
Ranganathan, A.[Ananth],
Semantic Scene Segmentation using Random Multinomial Logit,
BMVC09(xx-yy).
PDF Version.
0909
General multi-class classifier
based on an ensemble of multinomial logistic regression models.
BibRef
Yin, X.C.[Xu-Cheng],
Hao, H.W.[Hong-Wei],
Tang, Y.F.[Yun-Feng],
Sun, J.[Jun],
Naoi, S.[Satoshi],
Rejection Strategies with Multiple Classifiers for Handwritten
Character Recognition,
ICDAR09(1126-1130).
IEEE DOI Link
0907
BibRef
Parakhin, M.[Mikhail],
Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic
Pairwise Preferences,
ICDAR09(616-620).
IEEE DOI Link
0907
Ranking when pairwise ranking is inconsistent (not transitive).
apply to handwriting.
BibRef
Du, P.,
Sun, H.,
Zhang, W.,
Multiple classifier combination for target identification from high
resolution remote sensing image,
HighRes09(xx-yy).
PDF Version.
0906
BibRef
Sun, Y.[Ying],
Qi, H.R.[Hai-Rong],
Dynamic target classification in wireless sensor networks,
ICPR08(1-4).
IEEE DOI Link
0812
Combining results from different sensors
BibRef
Gupta, U.[Upavan],
Ranganathan, N.[Nagarajan],
A microeconomic approach to multi-objective spatial clustering,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Uchida, S.[Seiichi],
Amamoto, K.[Kazuma],
Early recognition of sequential patterns by classifier combination,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Kudo, M.[Mineichi],
Nakamura, A.[Atsuyoshi],
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ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Shidara, Y.[Yohji],
Kudo, M.[Mineichi],
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Classification by bagged consistent itemset rules,
ICPR08(1-4).
IEEE DOI Link
0812
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Nakamura, A.[Atsuyoshi],
Bagging, Random Subspace Method and Biding,
SSPR08(801-810).
Springer DOI Link
0812
BibRef
Su, X.Y.[Xiao-Yuan],
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Zhu, X.Q.[Xing-Quan],
VoB predictors: Voting on bagging classifications,
ICPR08(1-4).
IEEE DOI Link
0812
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Giacinto, G.[Giorgio],
Roli, F.[Fabio],
A Score Decidability Index for Dynamic Score Combination,
ICPR10(69-72).
IEEE DOI Link
1008
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Giacinto, G.[Giorgio],
Roli, F.[Fabio],
Dynamic score combination of binary experts,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
And:
Combination of Experts by Classifiers in Similarity Score Spaces,
SSPR08(821-830).
Springer DOI Link
0812
BibRef
Karnick, M.[Matthew],
Muhlbaier, M.D.[Michael D.],
Polikar, R.[Robi],
Incremental learning in non-stationary environments with concept drift
using a multiple classifier based approach,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
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Bao, C.Y.[Cheng-Yi],
Qiu, W.D.[Wei-Dong],
Bagging very weak learners with lazy local learning,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Yu, Z.W.[Zhi-Wen],
Deng, Z.K.[Zhong-Kai],
Wong, H.S.[Hau-San],
Identification of phosphorylation sites using a hybrid classifier
ensemble approach,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Byun, B.K.[Byung-Ki],
Ma, C.Y.[Cheng-Yuan],
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An experimental study on discriminative concept classifier combination
for TRECVID high-level feature extraction,
ICIP08(2532-2535).
IEEE DOI Link
0810
BibRef
Socorro, R.[Raisa],
Micó, L.[Luisa],
Use of Structured Pattern Representations for Combining Classifiers,
SSPR08(811-820).
Springer DOI Link
0812
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Martínez, F.[Francisco],
Fairhurst, M.[Michael],
Classifier Ensemble Generation for the Majority Vote Rule,
CIARP08(340-347).
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0809
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Vural, F.T.Y.[Fatos Tunay Yarman],
On the Performance of Stacked Generalization Classifiers,
ICIAR08(xx-yy).
Springer DOI Link
0806
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Maiti, C.[Chinmay],
Pal, S.[Somnath],
Efficient Multi-method Rule Learning for Pattern Classification Machine
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PReMI07(324-331).
Springer DOI Link
0712
BibRef
Kankanala, L.[Laxmi],
Murty, M.N.[M. Narasimha],
Hybrid Approaches for Clustering,
PReMI07(25-32).
Springer DOI Link
0712
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Ñanculef, R.[Ricardo],
Valle, C.[Carlos],
Allende, H.[Héctor],
Moraga, C.[Claudio],
Bagging with Asymmetric Costs for Misclassified and Correctly
Classified Examples,
CIARP07(694-703).
Springer DOI Link
0711
BibRef
Kyrgyzov, I.O.[Ivan O.],
Maitre, H.[Henri],
Campedel, M.[Marine],
A Method of Clustering Combination Applied to Satellite Image Analysis,
CIAP07(81-86).
IEEE DOI Link
0709
BibRef
Molinara, M.,
Ricamato, M.T.,
Tortorella, F.,
Facing Imbalanced Classes through Aggregation of Classifiers,
CIAP07(43-48).
IEEE DOI Link
0709
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Dimililer, N.[Nazife],
Varoglu, E.[Ekrem],
Altonçay, H.[Hakan],
Vote-Based Classifier Selection for Biomedical NER Using Genetic
Algorithms,
IbPRIA07(II: 202-209).
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0706
BibRef
Valdovinos, R.M.,
Sánchez, J.S.,
Gasca, E.,
Influence of Resampling and Weighting on Diversity and Accuracy of
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0706
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Valdovinos, R.M.,
Sánchez, J.S.,
Performance Analysis of Classifier Ensembles:
Neural Networks Versus Nearest Neighbor Rule,
IbPRIA07(I: 105-112).
Springer DOI Link
0706
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Mazón, J.N.[Jose-Norberto],
Micó, L.[Luisa],
Moreno-Seco, F.[Francisco],
New Neighborhood Based Classification Rules for Metric Spaces and Their
Use in Ensemble Classification,
IbPRIA07(I: 354-361).
Springer DOI Link
0706
BibRef
Raudys, S.J.[Sarunas J.],
Generalization Error of Multinomial Classifier,
SSPR06(502-511).
Springer DOI Link
0608
BibRef
Raudys, S.J.[Sarunas J.],
Denisov, V.[Vitalij],
Bielskis, A.A.[Antanas Andrius],
A Pool of Classifiers by SLP: A Multi-class Case,
ICIAR06(II: 47-56).
Springer DOI Link
0610
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Andra, S.[Srinivas],
Nagy, G.[George],
Combining Dichotomizers for MAP Field Classification,
ICPR06(IV: 210-214).
WWW Version.
0609
BibRef
Cheng, H.T.[Hsien-Ting],
Chen, C.S.[Chu-Song],
A Complementary Ordering Method for Class Imbalanced Problem,
ICPR06(III: 429-432).
WWW Version.
0609
Asymmetric Bagging with Vector Complementary Ordering.
Apply to biometrics.
BibRef
Viswanath, P.,
Jayasurya, K.[Karthik],
A Fast and Efficient Ensemble Clustering Method,
ICPR06(II: 720-723).
WWW Version.
0609
BibRef
Lefaucheur, P.[Patrice],
Nock, R.[Richard],
Robust Multiclass Ensemble Classifiers via Symmetric Functions,
ICPR06(IV: 136-139).
WWW Version.
0609
BibRef
Ekbal, A.[Asif],
Bandyopadhyay, S.[Sivaji],
Improving the Performance of a NER System by Post-processing and Voting,
SSPR08(831-841).
Springer DOI Link
0812
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Ekbal, A.[Asif],
Improvement of Prediction Accuracy Using Discretization and Voting
Classifier,
ICPR06(II: 695-698).
WWW Version.
0609
BibRef
Bauckhage, C.[Christian],
Kaster, T.[Thomas],
Benefits of Separable, Multilinear Discriminant Classification,
ICPR06(III: 1240-1243).
WWW Version.
0609
BibRef
And:
ICPR06(IV: 959).
WWW Version.
0609
BibRef
Bauckhage, C.[Christian],
Kaster, T.[Thomas],
Tsotsos, J.K.[John K.],
Applying Ensembles of Multilinear Classifiers in the Frequency Domain,
CVPR06(I: 95-102).
IEEE DOI Link
0606
BibRef
Bauckhage, C.[Christian],
Tsotsos, J.K.[John K.],
Separable Linear Discriminant Classification,
DAGM05(318).
Springer DOI Link
0509
BibRef
And:
Separable Linear Classifiers for Online Learning in Appearance Based
Object Detection,
CAIP05(347).
Springer DOI Link
0509
BibRef
Chellapilla, K.[Kumar],
Shilman, M.[Michael],
Simard, P.Y.[Patrice Y.],
Combining Multiple Classifiers for Faster Optical Character Recognition,
DAS06(358-367).
Springer DOI Link
0602
BibRef
Singh, R.[Rohit],
Samal, S.[Sandeep],
Lahiri, T.[Tapobrata],
A Novel Strategy for Designing Efficient Multiple Classifier,
ICB06(713-720).
Springer DOI Link
0601
BibRef
Valdovinos, R.M.[Rosa M.],
Salvador-Sánchez, J.,
Barandela, R.[Ricardo],
Dynamic and Static Weighting in Classifier Fusion,
IbPRIA05(II:59).
Springer DOI Link
0509
BibRef
Beattie, M.[Michael],
Vijaya Kumar, B.V.K.,
Lucey, S.[Simon],
Tonguz, O.K.[Ozan K.],
Combining Verification Decisions in a Multi-vendor Environment,
AVBPA05(406).
Springer DOI Link
0509
BibRef
Gutierrez, J.,
Rouas, J.L.,
Andre-Obrecht, R.,
Weighted loss functions to make risk-based language identification
fused decisions,
ICPR04(II: 863-866).
IEEE DOI Link
0409
BibRef
Reiter, S.,
Rigoll, G.,
Segmentation and classification of meeting events using multiple
classifier fusion and dynamic programming,
ICPR04(III: 434-437).
IEEE DOI Link
0409
BibRef
Jaeger, S.,
Informational classifier fusion,
ICPR04(I: 216-219).
IEEE DOI Link
0409
BibRef
Yi, X.[Xing],
Kou, Z.[Zhongbao],
Zhang, C.S.[Chang-Shui],
Classifer combination based on active learning,
ICPR04(I: 184-187).
IEEE DOI Link
0409
BibRef
Dmitry, V.,
Dmitry, K.,
Data dependent classifer fusion for construction of stable effective
algorithms,
ICPR04(I: 144-147).
IEEE DOI Link
0409
BibRef
Kang, H.J.[Hee-Joong],
Doermann, D.,
Selection of classifiers for the construction of multiple classifier
systems,
ICDAR05(II: 1194-1198).
IEEE DOI Link
0508
BibRef
Earlier:
Product Approximation by Minimizing the Upper Bound of Bayes Error Rate
for Bayesian Combination of Classifiers,
ICPR04(I: 252-255).
IEEE DOI Link
0409
BibRef
Earlier:
Evaluation of the information-theoretic construction of multiple
classifier systems,
ICDAR03(789-793).
IEEE Abstract.
0311
BibRef
Kang, H.J.,
Lee, S.W.,
An Information-theoretic Strategy for Constructing Multiple Classifier
Systems,
ICPR00(Vol II: 483-486).
IEEE DOI Link
0009
BibRef
Altinçay, H.[Hakan],
Çizili, B.[Buket],
Classifier Combination through Clustering in the Output Spaces,
CAIP03(487-493).
WWW Version.
0311
BibRef
Hamamura, T.,
Mizutani, H.,
Irie, B.,
A multiclass classification method based on multiple pairwise
classifiers,
ICDAR03(809-813).
IEEE Abstract.
0311
BibRef
de Stefano, C.[Claudio],
Fontanella, F.[Francesco],
di Freca, A.S.[Alessandra Scotto],
Marcelli, A.[Angelo],
Learning Bayesian Networks by Evolution for Classifier Combination,
ICDAR09(966-970).
IEEE DOI Link
0907
BibRef
de Stefano, C.[Claudio],
d'Elia, C.[Ciro],
Marcelli, A.[Angelo],
di Freca, A.S.[Alessandra Scotto],
Using Bayesian Network for combining classifiers,
CIAP07(73-80).
IEEE DOI Link
0709
BibRef
de Stefano, C.,
della Cioppa, A.,
Marcelli, A.,
Exploiting reliability for dynamic selection of classifiers by means of
genetic algorithms,
ICDAR03(671-675).
IEEE Abstract.
0311
BibRef
Earlier:
An adaptive weighted majority vote rule for combining multiple
classifiers,
ICPR02(II: 192-195).
IEEE DOI Link
0211
BibRef
Soto, A.,
A Probabilistic Approach for the Adaptive Integration of Multiple
Visual Cues Using an Agent Framework,
CMU-RI-TR-02-30, October, 2002.
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Ph.D.Thesis
HTML Version.
0306
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Kittler, J.V.[Josef V.],
Ahmadyfard, A.R.[Ali R.],
Multiple Classifier System Approach to Model Pruning in Object
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ECCV04(Vol IV: 342-353).
WWW Version.
0405
BibRef
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Kittler, J.V.[Josef V.],
A Multiple Classifier System Approach to Affine Invariant Object
Recognition,
CVS03(438 ff).
HTML Version.
0306
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Sirlantzis, K.,
Fairhurst, M.C.,
Guest, R.M.,
An evolutionary algorithm for classifier and combination rule selection
in multiple classifier systems,
ICPR02(II: 771-774).
IEEE DOI Link
0211
BibRef
Sirlantzis, K.,
Fairhurst, M.C.,
Optimisation of Multiple Classifier Systems Using Genetic Algorithms,
ICIP01(I: 1094-1097).
IEEE Abstract.
0108
BibRef
And:
Investigation of a novel self-configurable multiple classifier system
for character recognition,
ICDAR01(1002-1006).
IEEE DOI Link
0109
BibRef
Tax, D.M.J.,
Duin, R.P.W.,
Using two-class classifiers for multiclass classification,
ICPR02(II: 124-127).
IEEE DOI Link
0211
BibRef
Earlier:
Data Description in Subspaces,
ICPR00(Vol II: 672-675).
IEEE DOI Link
0009
BibRef
Skurichina, M.,
Ypma, A.,
Duin, R.P.W.,
The Role of Subclasses in Machine Diagnostics,
ICPR00(Vol II: 668-671).
IEEE DOI Link
0009
BibRef
Jeong, S.H.,
Lim, K.T.,
Nam, Y.S.,
A combination method of two classifiers based on the information of
confusion matrix,
FHR02(519-523).
IEEE Top Reference.
0209
BibRef
Iwayama, N.,
Akiyama, K.,
Ishigaki, K.,
Hybrid adaptation: integration of adaptive classification with adaptive
context processing,
FHR02(169-174).
IEEE Top Reference.
0209
BibRef
Mahamud, S.[Shyjan],
Hebert, M.[Martial],
Lafferty, J.[John],
Combining Simple Discriminators for Object Discrimination,
ECCV02(III: 776 ff.).
HTML Version.
0205
BibRef
Chawla, N.[Nitesh],
Moore, Jr., T.E.[Thomas E.],
Bowyer, K.W.[Kevin W.],
Hall, L.O.[Lawrence O.],
Springer, C.[Clayton], and
Kegelmeyer, P.[Philip],
Bagging Is a Small-Data-Set Phenomenon,
CVPR01(II:684-689).
IEEE Abstract.
0110
Form a committee of classifiers from subsets rather then
use bagging.
BibRef
Qian, Y.,
Suen, C.Y.,
Clustering Combination Method,
ICPR00(Vol II: 732-735).
IEEE DOI Link
0009
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Draper, B.A.[Bruce A.],
Baek, K.[Kyungim],
Bagging in Computer Vision,
CVPR98(144-149).
IEEE Abstract. Multiple predictors
BibRef
9800
le Capitaine, H.[Hoel],
Frélicot, C.[Carl],
An Optimum Class-Rejective Decision Rule and Its Evaluation,
ICPR10(3312-3315).
IEEE DOI Link
1008
BibRef
Earlier:
A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR
Operator,
SSPR08(612-621).
Springer DOI Link
0812
BibRef
And:
A class-selective rejection scheme based on blockwise similarity of
typicality degrees,
ICPR08(1-4).
IEEE DOI Link
0812
BibRef
Mascarilla, L.,
Frélicot, C.,
Another Look at Combining Rejection-based Pattern Classifiers,
ICPR00(Vol II: 156-159).
IEEE DOI Link
0009
BibRef
DeCarlo, D.[Douglas],
Metaxas, D.[Dimitris],
Combining Information using Hard Constraints,
CVPR99(II: 132-138).
IEEE Abstract.
WWW Version. Use hard constraints rather than statistical combination.
BibRef
9900
Kang, H.J.,
Kim, J.H.,
A Probabilistic Framework for Combining Multiple Classifiers at
Abstract Level,
ICDAR97(870-874).
IEEE DOI Link
9708
BibRef
Prevost, L.,
Milgram, M.,
Static and Dynamic Classifier Fusion for Character Recognition,
ICDAR97(499-506).
IEEE DOI Link
9708
BibRef
Franke, J.,
Mandler, E.,
A comparison of two approaches for combining the votes of cooperating
classifiers,
ICPR92(II:611-614).
IEEE DOI Link
9208
BibRef
Wong, G.,
Frei, H.P.,
Object recognition: the utopian method is dead; the time for combining
simple methods has come,
ICPR92(III:185-188).
IEEE DOI Link
9208
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hierarchical Combination, Multi-Stage Classifiers .