메뉴 건너뛰기




Volumn 37, Issue 1, 2014, Pages 4-14

Pattern classification and clustering: A review of partially supervised learning approaches

Author keywords

Active learning; Multi view learning; Neural network; Partially supervised learning; Semi supervised learning; Transductive learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; FUZZY NEURAL NETWORKS; NEURAL NETWORKS; PATTERN RECOGNITION; SUPERVISED LEARNING;

EID: 84891629945     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.10.017     Document Type: Article
Times cited : (194)

References (118)
  • 1
    • 77958454206 scopus 로고    scopus 로고
    • Genetic algorithm based training for semi-supervised SVM
    • M. Adankon, and M. Cheriet Genetic algorithm based training for semi-supervised SVM Neural Computing and Applications 19 2010 1197 1206
    • (2010) Neural Computing and Applications , vol.19 , pp. 1197-1206
    • Adankon, M.1    Cheriet, M.2
  • 3
    • 57349126313 scopus 로고    scopus 로고
    • Inter-coder agreement for computational linguistics
    • R. Artstein, and M. Poesio Inter-coder agreement for computational linguistics Computational Linguistics 34 4 2008 555 596
    • (2008) Computational Linguistics , vol.34 , Issue.4 , pp. 555-596
    • Artstein, R.1    Poesio, M.2
  • 7
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani Manifold regularization: a geometric framework for learning from labeled and unlabeled examples Journal of Machine Learning Research 7 2006 2399 2434 (Pubitemid 44708005)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 9
    • 67049145116 scopus 로고    scopus 로고
    • Semi-supervised learning using semi-definite programming
    • Chapelle O., B. Schölkopf, A. Zien, MIT Press
    • T.D. Bie, and N. Cristianini Semi-supervised learning using semi-definite programming Chapelle O., B. Schölkopf, A. Zien, Semi-Supervised Learning 2006 MIT Press 119 135
    • (2006) Semi-Supervised Learning , pp. 119-135
    • Bie, T.D.1    Cristianini, N.2
  • 17
    • 41549144249 scopus 로고    scopus 로고
    • Optimization techniques for semi-supervised support vector machines
    • O. Chapelle, V. Sindhwani, and S.S. Keerthi Optimization techniques for semi-supervised support vector machines The Journal of Machine Learning Research 9 2008 203 233 (Pubitemid 351469022)
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 203-233
    • Chapelle, O.1    Sindhwani, V.2    Keerthi, S.S.3
  • 20
    • 56449094571 scopus 로고    scopus 로고
    • Semi-supervised and active learning with the probabilistic RBF classifier
    • C. Constantinopoulos, and A. Likas Semi-supervised and active learning with the probabilistic RBF classifier Neurocomputing 71 13-15 2008 2489 2498
    • (2008) Neurocomputing , vol.71 , Issue.1315 , pp. 2489-2498
    • Constantinopoulos, C.1    Likas, A.2
  • 24
    • 84857385749 scopus 로고    scopus 로고
    • Semi-supervised kernel clustering with sample-to-cluster weights
    • F. Schwenker, E. Trentin, LNAI Springer
    • S. Faußer, and F. Schwenker Semi-supervised kernel clustering with sample-to-cluster weights F. Schwenker, E. Trentin, Partially Supervised Learning (PSL'11) LNAI vol. 7081 2012 Springer 72 81
    • (2012) Partially Supervised Learning (PSL'11) , vol.7081 , pp. 72-81
    • Faußer, S.1    Schwenker, F.2
  • 25
    • 84875251066 scopus 로고    scopus 로고
    • A neural network algorithm for semi-supervised node label learning from unbalanced data
    • M. Frasca, A. Bertoni, M. Re, and G. Valentini A neural network algorithm for semi-supervised node label learning from unbalanced data Neural Networks 43 2013 84 98
    • (2013) Neural Networks , vol.43 , pp. 84-98
    • Frasca, M.1    Bertoni, A.2    Re, M.3    Valentini, G.4
  • 26
    • 0031209604 scopus 로고    scopus 로고
    • Selective sampling using the query by committee algorithm
    • Y. Freund, H. Seung, E. Shamir, and N. Tishby Selective sampling using the query by committee algorithm Machine Learning 28 2-3 1997 133 168 (Pubitemid 127506338)
    • (1997) Machine Learning , vol.28 , Issue.2-3 , pp. 133-168
    • Freund, Y.1    Seung, H.S.2    Shamir, E.3    Tishby, N.4
  • 27
    • 71249160512 scopus 로고    scopus 로고
    • Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition
    • IEEE Computer Society Washington, DC, USA
    • V. Frinken, and H. Bunke Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition Proc. of the 10th International Conference on Document Analysis and Recognition (ICDAR'09) 2009 IEEE Computer Society Washington, DC, USA 31 35
    • (2009) Proc. of the 10th International Conference on Document Analysis and Recognition (ICDAR'09) , pp. 31-35
    • Frinken, V.1    Bunke, H.2
  • 29
    • 0036454664 scopus 로고    scopus 로고
    • Semi-supervised support vector machines for unlabeled data classification
    • G. Fung, and O. Mangasarian Semi-supervised support vector machines for unlabeled data classification Optimization Methods and Software 15 2001 29 44 (Pubitemid 33817502)
    • (2001) Optimization Methods and Software , vol.15 , Issue.1 , pp. 29-44
    • Fung, G.1    Mangasarian, O.L.2
  • 34
    • 84891632971 scopus 로고    scopus 로고
    • A novel hybrid neural network for data clustering
    • H.R. Arabnia, M. Dehmer, F. Emmert-Streib, M.Q. Yang, CSREA Press
    • D. Guan, A. Gavrilov, W. Yuan, Y.-K. Lee, and S. Lee A novel hybrid neural network for data clustering H.R. Arabnia, M. Dehmer, F. Emmert-Streib, M.Q. Yang, MLMTA 2007 CSREA Press 284 288
    • (2007) MLMTA , pp. 284-288
    • Guan, D.1    Gavrilov, A.2    Yuan, W.3    Lee, Y.-K.4    Lee, S.5
  • 38
    • 77950296222 scopus 로고    scopus 로고
    • Semi-supervised learning for tree-structured ensembles of RBF networks with co-training
    • M.F.A. Hady, F. Schwenker, and G. Palm Semi-supervised learning for tree-structured ensembles of RBF networks with co-training Neural Networks 23 4 2010 497 509
    • (2010) Neural Networks , vol.23 , Issue.4 , pp. 497-509
    • Hady, M.F.A.1    Schwenker, F.2    Palm, G.3
  • 44
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond k-means
    • A.K. Jain Data clustering: 50 years beyond k-means Pattern Recognition Letters 31 8 2010 651 666
    • (2010) Pattern Recognition Letters , vol.31 , Issue.8 , pp. 651-666
    • Jain, A.K.1
  • 46
    • 70349876750 scopus 로고    scopus 로고
    • Semi-supervised text classification using RBF networks
    • N.M. Adams, C. Robardet, A. Siebes, J.-F. Boulicaut, Springer
    • E.P. Jiang Semi-supervised text classification using RBF networks N.M. Adams, C. Robardet, A. Siebes, J.-F. Boulicaut, IDA vol. 5772 2009 Springer 95 106
    • (2009) IDA , vol.5772 , pp. 95-106
    • Jiang, E.P.1
  • 48
    • 78751645408 scopus 로고    scopus 로고
    • Constraint scores for semi-supervised feature selection: A comparative study
    • M. Kalakech, P. Biela, L. Macaire, and D. Hamad Constraint scores for semi-supervised feature selection: a comparative study Pattern Recognition Letters 32 5 2011 656 665
    • (2011) Pattern Recognition Letters , vol.32 , Issue.5 , pp. 656-665
    • Kalakech, M.1    Biela, P.2    MacAire, L.3    Hamad, D.4
  • 50
    • 0031274467 scopus 로고    scopus 로고
    • An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
    • PII S1063670697048364
    • N. Karayiannis, and J. Bezdek An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering IEEE Transactions on Fuzzy Systems 5 4 1997 622 628 (Pubitemid 127768540)
    • (1997) IEEE Transactions on Fuzzy Systems , vol.5 , Issue.4 , pp. 622-628
    • Karayiannis, N.B.1    Bezdek, J.C.2
  • 52
    • 58149202361 scopus 로고    scopus 로고
    • Semi-supervised graph clustering: A kernel approach
    • B. Kulis, S. Basu, I. Dhillon, and R. Mooney Semi-supervised graph clustering: a kernel approach Machine Learning 74 1 2009 1 22
    • (2009) Machine Learning , vol.74 , Issue.1 , pp. 1-22
    • Kulis, B.1    Basu, S.2    Dhillon, I.3    Mooney, R.4
  • 53
    • 74549174193 scopus 로고    scopus 로고
    • Semi-supervised nonnegative matrix factorization
    • H. Lee, J. Yoo, and S. Choi Semi-supervised nonnegative matrix factorization IEEE Signal Processing Letters 17 1 2010 4 7
    • (2010) IEEE Signal Processing Letters , vol.17 , Issue.1 , pp. 4-7
    • Lee, H.1    Yoo, J.2    Choi, S.3
  • 56
    • 36249007597 scopus 로고    scopus 로고
    • Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples
    • DOI 10.1109/TSMCA.2007.904745
    • M. Li, and Z.-H. Zhou Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans 37 6 2007 1088 1098 (Pubitemid 350130846)
    • (2007) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans , vol.37 , Issue.6 , pp. 1088-1098
    • Li, M.1    Zhou, Z.-H.2
  • 57
    • 43249086679 scopus 로고    scopus 로고
    • A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system
    • Y. Li, C. Guan, H. Li, and Z. Chin A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system Pattern Recognition Letters 29 9 2008 1285 1294
    • (2008) Pattern Recognition Letters , vol.29 , Issue.9 , pp. 1285-1294
    • Li, Y.1    Guan, C.2    Li, H.3    Chin, Z.4
  • 58
    • 1242352526 scopus 로고    scopus 로고
    • Selective sampling for nearest neighbor classifiers
    • M. Lindenbaum, S. Markovitch, and D. Rusakov Selective sampling for nearest neighbor classifiers Machine Learning 54 2 2004 125 152
    • (2004) Machine Learning , vol.54 , Issue.2 , pp. 125-152
    • Lindenbaum, M.1    Markovitch, S.2    Rusakov, D.3
  • 59
    • 57949110234 scopus 로고    scopus 로고
    • A semi-supervised learning based relevance feedback algorithm in content-based image retrieval
    • Luo, Z.-P., Zhang, X.-M., 2008. A semi-supervised learning based relevance feedback algorithm in content-based image retrieval. In: Chinese Conference on Pattern Recognition (CCPR '08), pp. 1-4.
    • (2008) Chinese Conference on Pattern Recognition (CCPR '08) , pp. 1-4
    • Luo, Z.-P.1    Zhang, X.-M.2
  • 60
    • 70450196429 scopus 로고    scopus 로고
    • On the semi-supervised learning of multi-layered perceptrons
    • ISCA
    • J. Malkin, A. Subramanya, and J. Bilmes On the semi-supervised learning of multi-layered perceptrons INTERSPEECH 2009 ISCA 660 663
    • (2009) Interspeech , pp. 660-663
    • Malkin, J.1    Subramanya, A.2    Bilmes, J.3
  • 62
    • 84867629933 scopus 로고    scopus 로고
    • On instance selection in audio based emotion recognition
    • N. Mana, F. Schwenker, E. Trentin, LNAI Springer
    • S. Meudt, and F. Schwenker On instance selection in audio based emotion recognition N. Mana, F. Schwenker, E. Trentin, Artificial Neural Networks in Pattern Recognition (ANNPR'12) LNAI vol. 7477 2012 Springer 186 192
    • (2012) Artificial Neural Networks in Pattern Recognition (ANNPR'12) , vol.7477 , pp. 186-192
    • Meudt, S.1    Schwenker, F.2
  • 63
    • 84898980291 scopus 로고    scopus 로고
    • A mixture of experts classifier with learning based on both labelled and unlabelled data
    • D.J. Miller, and H.S. Uyar A mixture of experts classifier with learning based on both labelled and unlabelled data Advances in Neural Information Processing Systems 9 1997 571 577
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 571-577
    • Miller, D.J.1    Uyar, H.S.2
  • 65
    • 0346083482 scopus 로고    scopus 로고
    • Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
    • Nigam, K., 2001. Using unlabeled data to improve text classification. Ph.D. Thesis, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA.
    • (2001) Using Unlabeled Data to Improve Text Classification
    • Nigam, K.1
  • 67
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • K. Nigam, A.K. McCallum, S. Thrun, and T. Mitchell Text classification from labeled and unlabeled documents using EM Machine Learning 39 2-3 2000 103 134 (Pubitemid 30594822)
    • (2000) Machine Learning , vol.39 , Issue.2 , pp. 103-134
    • Nigam, K.1    Mccallum, A.K.2    Thrun, S.3    Mitchell, T.4
  • 68
    • 84859302340 scopus 로고    scopus 로고
    • A cluster-assumption based batch mode active learning technique
    • S. Patra, and L. Bruzzone A cluster-assumption based batch mode active learning technique Pattern Recognition Letters 33 9 2012 1042 1048
    • (2012) Pattern Recognition Letters , vol.33 , Issue.9 , pp. 1042-1048
    • Patra, S.1    Bruzzone, L.2
  • 69
    • 38149136506 scopus 로고    scopus 로고
    • Semi-supervised learning with multilayer perceptron for detecting changes of remote sensing images
    • A. Ghosh, R. De, S. Pal, Springer Berlin, Heidelberg
    • S. Patra, S. Ghosh, and A. Ghosh Semi-supervised learning with multilayer perceptron for detecting changes of remote sensing images A. Ghosh, R. De, S. Pal, Pattern Recognition and Machine Intelligence vol. 4815 2007 Springer Berlin, Heidelberg 161 168
    • (2007) Pattern Recognition and Machine Intelligence , vol.4815 , pp. 161-168
    • Patra, S.1    Ghosh, S.2    Ghosh, A.3
  • 70
    • 68749110470 scopus 로고    scopus 로고
    • Recognizing body poses using multilinear analysis and semi-supervised learning
    • B. Peng, G. Qian, and Y. Ma Recognizing body poses using multilinear analysis and semi-supervised learning Pattern Recognition Letters 30 14 2009 1289 1294
    • (2009) Pattern Recognition Letters , vol.30 , Issue.14 , pp. 1289-1294
    • Peng, B.1    Qian, G.2    Ma, Y.3
  • 71
    • 0003243224 scopus 로고    scopus 로고
    • Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
    • MIT Press
    • J.C. Platt Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers 1999 MIT Press 61 74
    • (1999) Advances in Large Margin Classifiers , pp. 61-74
    • Platt, J.C.1
  • 73
    • 77956497661 scopus 로고    scopus 로고
    • Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins
    • Y. Qi, O. Tastan, J.G. Carbonell, J. Klein-Seetharaman, and J. Weston Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins Bioinformatics 26 18 2010 i645 i652
    • (2010) Bioinformatics , vol.26 , Issue.18
    • Qi, Y.1    Tastan, O.2    Carbonell, J.G.3    Klein-Seetharaman, J.4    Weston, J.5
  • 76
    • 84958546669 scopus 로고    scopus 로고
    • Active hidden markov models for information extraction
    • Advances in Intelligent Data Analysis
    • T. Scheffer, C. Decomain, and S. Wrobel Active hidden Markov models for information extraction F. Hoffmann, D.J. Hand, N.M. Adams, D.H. Fisher, G. Guimarães, Advances in Intelligent Data Analysis (IDA'01) LNCS vol. 2189 2001 Springer 309 318 (Pubitemid 33348510)
    • (2001) Lecture Notes In Computer Science , Issue.2189 , pp. 309-318
    • Scheffer, T.1    Decomain, C.2    Wrobel, S.3
  • 78
    • 84867329306 scopus 로고    scopus 로고
    • Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification
    • S. Scherer, J. Kane, C. Gobl, and F. Schwenker Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification Computer Speech & Language 27 1 2013 263 287
    • (2013) Computer Speech & Language , vol.27 , Issue.1 , pp. 263-287
    • Scherer, S.1    Kane, J.2    Gobl, C.3    Schwenker, F.4
  • 79
    • 0035015680 scopus 로고    scopus 로고
    • Three learning phases for radial-basis-function networks
    • DOI 10.1016/S0893-6080(01)00027-2, PII S0893608001000272
    • F. Schwenker, H.A. Kestler, and G. Palm Three learning phases for radial-basis-function networks Neural Networks 14 4-5 2001 439 458 (Pubitemid 32475857)
    • (2001) Neural Networks , vol.14 , Issue.4-5 , pp. 439-458
    • Schwenker, F.1    Kestler, H.A.2    Palm, G.3
  • 80
    • 0005977840 scopus 로고    scopus 로고
    • Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation
    • Seeger, M., 2002. Learning with labeled and unlabeled data. Technical Report, University of Edinburgh, Institute for Adaptive and Neural Computation.
    • (2002) Learning with Labeled and Unlabeled Data
    • Seeger, M.1
  • 82
    • 68949137209 scopus 로고    scopus 로고
    • Tech. rep., Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI
    • Settles, B., 2009. Active learning literature survey. Tech. rep., Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI.
    • (2009) Active Learning Literature Survey
    • Settles, B.1
  • 85
    • 0028499630 scopus 로고
    • The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
    • B. Shahshahani, and D. Landgrebe The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon IEEE Transactions on Geoscience and Remote Sensing 32 5 1994 1087 1095
    • (1994) IEEE Transactions on Geoscience and Remote Sensing , vol.32 , Issue.5 , pp. 1087-1095
    • Shahshahani, B.1    Landgrebe, D.2
  • 86
    • 80052816923 scopus 로고    scopus 로고
    • An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network
    • F. Shen, H. Yu, K. Sakurai, and O. Hasegawa An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network Neural Computing and Applications 20 7 2011 1061 1074
    • (2011) Neural Computing and Applications , vol.20 , Issue.7 , pp. 1061-1074
    • Shen, F.1    Yu, H.2    Sakurai, K.3    Hasegawa, O.4
  • 89
    • 77949267074 scopus 로고    scopus 로고
    • Kernel-based metric learning for semi-supervised clustering
    • M. Soleymani Baghshah, and S. Bagheri Shouraki Kernel-based metric learning for semi-supervised clustering Neurocomputing 73 7 2010 1352 1361
    • (2010) Neurocomputing , vol.73 , Issue.7 , pp. 1352-1361
    • Soleymani Baghshah, M.1    Bagheri Shouraki, S.2
  • 90
    • 48649090541 scopus 로고    scopus 로고
    • Semi-supervised sub-manifold discriminant analysis
    • Y. Song, F. Nie, and C. Zhang Semi-supervised sub-manifold discriminant analysis Pattern Recognition Letters 29 13 2008 1806 1813
    • (2008) Pattern Recognition Letters , vol.29 , Issue.13 , pp. 1806-1813
    • Song, Y.1    Nie, F.2    Zhang, C.3
  • 91
    • 72749118330 scopus 로고    scopus 로고
    • Comparison of neural classification algorithms applied to land cover mapping
    • B. Apolloni, S. Bassis, M. Marinaro, Frontiers in Artificial Intelligence and Applications IOS Press
    • C. Thiel, F. Giacco, F. Schwenker, and G. Palm Comparison of neural classification algorithms applied to land cover mapping B. Apolloni, S. Bassis, M. Marinaro, New Directions in Neural Networks (WIRN 2008) Frontiers in Artificial Intelligence and Applications vol. 193 2009 IOS Press 254 263
    • (2009) New Directions in Neural Networks (WIRN 2008) , vol.193 , pp. 254-263
    • Thiel, C.1    Giacco, F.2    Schwenker, F.3    Palm, G.4
  • 95
    • 0042868698 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong, and D. Koller Support vector machine active learning with applications to text classification Journal Machine Learning Research 2 2002 45 66
    • (2002) Journal Machine Learning Research , vol.2 , pp. 45-66
    • Tong, S.1    Koller, D.2
  • 100
    • 0035576170 scopus 로고    scopus 로고
    • Using unlabelled data to train a multilayer perceptron
    • DOI 10.1023/A:1012707515770
    • A. Verikas, A. Gelzinis, and K. Malmqvist Using unlabelled data to train a multilayer perceptron Neural Processing Letters 14 3 2001 179 201 (Pubitemid 33110850)
    • (2001) Neural Processing Letters , vol.14 , Issue.3 , pp. 179-201
    • Verikas, A.1    Gelzinis, A.2    Malmqvist, K.3
  • 105
    • 0015397010 scopus 로고
    • On decision directed estimation and stochastic approximation
    • T. Young, and A. Farjo On decision directed estimation and stochastic approximation IEEE Transactions on Information Theory 18 5 1972 671 673
    • (1972) IEEE Transactions on Information Theory , vol.18 , Issue.5 , pp. 671-673
    • Young, T.1    Farjo, A.2
  • 106
    • 77956057508 scopus 로고    scopus 로고
    • Question classification based on co-training style semi-supervised learning
    • Z. Yu, L. Su, L. Li, Q. Zhao, C. Mao, and J. Guo Question classification based on co-training style semi-supervised learning Pattern Recognition Letters 31 13 2010 1975 1980
    • (2010) Pattern Recognition Letters , vol.31 , Issue.13 , pp. 1975-1980
    • Yu, Z.1    Su, L.2    Li, L.3    Zhao, Q.4    Mao, C.5    Guo, J.6
  • 107
    • 84873621241 scopus 로고    scopus 로고
    • Contextual and active learning-based affect-sensing from virtual drama improvisation
    • L. Zhang Contextual and active learning-based affect-sensing from virtual drama improvisation ACM Transaction on Speech Language Processing 9 4 2013 8:1 8:25
    • (2013) ACM Transaction on Speech Language Processing , vol.9 , Issue.4 , pp. 81-825
    • Zhang, L.1
  • 108
    • 78650197593 scopus 로고    scopus 로고
    • Multiple-view multiple-learner active learning
    • Q. Zhang, and S. Sun Multiple-view multiple-learner active learning Pattern Recognition 43 9 2010 3113 3119
    • (2010) Pattern Recognition , vol.43 , Issue.9 , pp. 3113-3119
    • Zhang, Q.1    Sun, S.2
  • 111
    • 28244448186 scopus 로고    scopus 로고
    • Tri-training: Exploiting unlabeled data using three classifiers
    • DOI 10.1109/TKDE.2005.186
    • Z.-H. Zhou, and M. Li Tri-training: exploiting unlabeled data using three classifiers IEEE Transactions on Knowledge and Data Engineering 17 11 2005 1529 1541 (Pubitemid 41704840)
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.11 , pp. 1529-1541
    • Zhou, Z.-H.1    Li, M.2
  • 113
    • 77956708689 scopus 로고    scopus 로고
    • Semi-supervised learning by disagreement
    • Z.-H. Zhou, and M. Li Semi-supervised learning by disagreement Knowledge and Information Systems 24 3 2010 415 439
    • (2010) Knowledge and Information Systems , vol.24 , Issue.3 , pp. 415-439
    • Zhou, Z.-H.1    Li, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.