RETRACTED: UFuzzy: Fuzzy Models with Universum

Applied Soft Computing - Tập 52 - Trang 1296-1315 - 2017
L. Tencer1, M. Reznakova1, M. Cheriet1
1École de technologie supérieure, Synchromedia Lab, Montreal, Quebec, Canada 396-8800

Tài liệu tham khảo

Wolpert, 1997, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1, 67, 10.1109/4235.585893 Bengio, 2009, 2 Erhan, 2010, Why does unsupervised pre-training help deep learning?, J. Mach. Learn. Res., 11, 625 Bai, 2010, Learning context-sensitive shape similarity by graph transduction, IEEE Trans. Pattern Anal. Mach. Intell., 32, 861 Tencer, 2013, Sketch-based retrieval of document illustrations and regions of interest, 728 Weston, 2006, Inference with the Universum, 1009 Wang, 2011, Learning context-sensitive similarity by shortest path propagation, Pattern Recognit., 44, 2367, 10.1016/j.patcog.2011.02.007 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Shen, 2012, UBoost: Boosting with the universum, IEEE Trans. Pattern Anal. Mach. Intell., 34, 825, 10.1109/TPAMI.2011.240 Fournier, 2002, Long-term similarity learning in content-based image retrieval, 441 Nigam, 2000, Text classification from labeled and unlabeled documents using EM, Mach. Learn., 39, 103, 10.1023/A:1007692713085 Muslea, 2000, Selective sampling with redundant views, 621 Vapnik, 1998, 1 Zhu, 2005 Mitchell, 1998, Combining labeled and unlabeled data with co-training, 92 Goldman, 2000, Enhancing supervised learning with unlabeled data, 327 Zhou, 2004, Democratic co-learning, 594 Zhou, 2005, Tri-training: exploiting unlabeled data using three classifiers, IEEE Trans. Knowl. Data Eng., 17, 1529, 10.1109/TKDE.2005.186 Li, 2007, Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples, IEEE Trans. Syst. Man Cybern. A: Syst. Hum., 37, 1088, 10.1109/TSMCA.2007.904745 Yarowsky, 1995, Unsupervised word sense disambiguation rivaling supervised methods, 189 Riloff, 2003, Learning subjective nouns using extraction pattern bootstrapping, 25 Maeireizo, 2004, Co-training for predicting emotions with spoken dialogue data Lomsadze, 2005, Gene identification in novel eukaryotic genomes by self-training algorithm, Nucl. Acids Res., 33, 6494, 10.1093/nar/gki937 Rosenberg, 2007, Semi-supervised self-training of object detection models, 29 McClosky, 2006, Effective self-training for parsing, 152 McClosky, 2008, Self-training for biomedical parsing, 3 Adankon, 2011, Help-Training for semi-supervised support vector machines, 2220 Adankon, 2009, Help-training semi-supervised LS-SVM, 49 Adankon, 2008, Help-training for semi-supervised discriminative classifiers. Application to SVM, 1 Adankon, 2009, Model selection for the LS-SVM. Application to handwriting recognition, Pattern Recognit., 42, 3264, 10.1016/j.patcog.2008.10.023 Partalas, 2010, An ensemble uncertainty aware measure for directed hill climbing ensemble pruning, Mach. Learn., 81, 257, 10.1007/s10994-010-5172-0 Dai, 2013, A competitive ensemble pruning approach based on cross-validation technique, Knowl.-Based Syst., 37, 394, 10.1016/j.knosys.2012.08.024 Liu, 2014, Ensemble selection by GRASP, Appl. Intell., 1 Dai, 2015, Introducing randomness into greedy ensemble pruning algorithms, Appl. Intell., 42, 406, 10.1007/s10489-014-0605-2 Raina, 2003, Classification with hybrid generative/discriminative models Bishop, 2007, Generative or discriminative? Getting the best of both worlds, Bayesian Stat., 8, 3 Fujino, 2008, Semisupervised learning for a hybrid generative/discriminative classifier based on the maximum entropy principle, IEEE Trans. Pattern Anal. Mach. Intell., 30, 424, 10.1109/TPAMI.2007.70710 Forster, 2005 Chapelle, 2006, vol. 2 Zhu, 2006, Semi-supervised learning literature survey Seeger, 2002 Schwenker, 2014, Partially supervised learning for pattern recognition, Pattern Recognit. Lett., 37, 1, 10.1016/j.patrec.2013.10.014 Schwenker, 2014, Pattern classification and clustering: a review of partially supervised learning approaches, Pattern Recognit. Lett., 37, 4, 10.1016/j.patrec.2013.10.017 Moon, 1996, The expectation-maximization algorithm, IEEE Signal Process. Mag., 13, 47, 10.1109/79.543975 Muslea, 2002, Active + Semi-Supervised Learning = Robust Multi-View Learning Li, 2010, Two-view transductive support vector machines, 235 Bruzzone, 2006, A novel transductive SVM for semisupervised classification of remote-sensing images, IEEE Trans. Geosci. Remote Sens., 44, 3363, 10.1109/TGRS.2006.877950 Blum, 2001, Learning from labeled and unlabeled data using graph mincuts Zhu, 2003, Semi-supervised learning using gaussian fields and harmonic functions Zhu, 2003, Combining active learning and semi-supervised learning using gaussian fields and harmonic functions Zhou, 2004, Learning with local and global consistency, Adv. Neural Inf. Process. Syst. 16, 1 Belkin, 2006, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples, J. Mach. Learn. Res., 7, 2399 Zhou, 2010, Semi-supervised learning by disagreement, Knowl. Inf. Syst., 24, 415, 10.1007/s10115-009-0209-z Hady, 2008, Co-training by committee: a new semi-supervised learning framework, 563 Tur, 2005, Combining active and semi-supervised learning for spoken language understanding, Speech Commun., 45, 171, 10.1016/j.specom.2004.08.002 Ando, 2005, A high-performance semi-supervised learning method for text chunking, 1 Mallapragada, 2009, SemiBoost: Boosting for semi-supervised learning, IEEE Trans. Pattern Anal. Mach. Intell., 31, 2000, 10.1109/TPAMI.2008.235 Abdel Hady, 2010, Combining committee-based semi-supervised learning and active learning, J. Comput. Sci. Technol., 25, 681, 10.1007/s11390-010-9357-6 Zhou, 2011, When semi-supervised learning meets ensemble learning, Front. Electr. Electron. Eng. China, 6, 6, 10.1007/s11460-011-0126-2 Woźniak, 2014, A survey of multiple classifier systems as hybrid systems, Inf. Fusion, 16, 3, 10.1016/j.inffus.2013.04.006 Leng, 2013, Combining active learning and semi-supervised learning to construct SVM classifier, Knowl.-Based Syst., 44, 121, 10.1016/j.knosys.2013.01.032 Zhou, 2013, Active deep learning method for semi-supervised sentiment classification, Neurocomputing, 120, 536, 10.1016/j.neucom.2013.04.017 Saito, 2014, An active learning paradigm based on a priori data reduction and organization, Expert Syst. Appl., 41, 6086, 10.1016/j.eswa.2014.04.007 Zhang, 2014, Semi-supervised learning combining co-training with active learning, Expert Syst. Appl., 41, 2372, 10.1016/j.eswa.2013.09.035 Zhang, 2015, Cooperative learning and its application to emotion recognition from speech, IEEE Trans. Audio Speech Lang. Process., 23, 115 Yang, 2015, Multi-class active learning by uncertainty sampling with diversity maximization, Int. J. Comput. Vis., 113, 113, 10.1007/s11263-014-0781-x Reitmaier, 2015, Transductive active learning – a new semi-supervised learning approach based on iteratively refined generative models to capture structure in data, Inf. Sci., 293, 275, 10.1016/j.ins.2014.09.009 Zhuang, 2012, Non-negative low rank and sparse graph for semi-supervised learning, 2328 Weston, 2012, Deep learning via semi-supervised embedding, 639 Kingma, 2014, Semi-supervised learning with deep generative models, 1 Dai, 2015, Semi-supervised Sequence Learning, 1 Huang, 2014, Semi-supervised and unsupervised extreme learning machines, IEEE Trans. Cybern., 44, 2405, 10.1109/TCYB.2014.2307349 Li, 2005, SETRED: self-training with editing, Adv. Knowl. Discov. Data Mining, 611, 10.1007/11430919_71 Adankon, 2009, Semisupervised least squares support vector machine, IEEE Trans. Neural Netw./Publ. IEEE Neural Netw. Council, 20, 1858, 10.1109/TNN.2009.2031143 Settles, 2010 Angluin, 1988, Queries and concept learning, Mach. Learn., 2, 319, 10.1007/BF00116828 Atlas, 1989, Training connectionist networks with queries and selective sampling, Adv. Neural Inf. Process. Syst., 2, 566 Lewis, 1994, A sequential algorithm for training text classifiers, 10 Seung, 1992, Query by committee, 287 Settles, 2008, An analysis of active learning strategies for sequence labeling tasks, 1070 Settles, 2007, Multiple-instance active learning, Adv. Neural Inf. Process. Syst. 20, 1 Roy, 2001, Toward optimal active learning through monte carlo estimation of error reduction Singh, 2009, Unlabeled data: now it helps, now it doesnt, Adv. Neural Inf. Process. Syst. 21, 1513 Rigollet, 2007, Generalization error bounds in semi-supervised classification under the cluster assumption, J. Mach. Learn. Res., 8, 1369 Lafferty, 2006, 1 Chawla, 2004, Editorial: Special Issue on Learning from Imbalanced Data Sets, ACM SIGKDD Explor. Newslett., 6, 1, 10.1145/1007730.1007733 Huang, 2004, Semi-supervised learning from unbalanced labeled data – an improvement, 802 Li, 2011, Semi-supervised learning for imbalanced sentiment classification, 1826 Frasca, 2013, A neural network algorithm for semi-supervised node label learning from unbalanced data, Neural Netw., 43, 84, 10.1016/j.neunet.2013.01.021 Hastie, 2009, vol. 27 Bottou, 1998, Online learning and stochastic approximations, On-line Learn. Neural Netw., 1 Crammer, 2003, Online passive-aggressive algorithms, J. Mach. Learn. Res., 7, 551 Fan, 2008, LIBLINEAR: A library for large linear classification, J. Mach. Learn. Res., 9, 1871 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans. Intell. Syst. Technol., 2, 27:1, 10.1145/1961189.1961199 Bishop, 2006, vol. 1 Breiman, 1984 Breiman, 2001, Random forests, Mach. Learn., 5, 10.1023/A:1010933404324 Freund, 1995, A desicion-theoretic generalization of on-line learning and an application to boosting, Comput. Learn. Theory, 55, 119 Asuncion, 2007 Renakova, 2013, ARTIST: ART-2A driven generation of fuzzy rules for online handwritten gesture recognition, 354 Režnáková, 2012, Online handwritten gesture recognition based on Takagi-Sugeno fuzzy models, 1247 Bergstra, 2012, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281