On the Power of Ensemble: Supervised and Unsupervised Methods Reconciled*

[1]  Xiaoli Z. Fern,et al.  Cluster Ensemble Selection , 2008, Stat. Anal. Data Min..

[2]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[3]  Leslie Pack Kaelbling,et al.  Efficient Bayesian Task-Level Transfer Learning , 2007, IJCAI.

[4]  Kun Zhang,et al.  Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions , 2006, Sixth International Conference on Data Mining (ICDM'06).

[5]  Steffen Bickel,et al.  Multi-view clustering , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[6]  Sanjoy Dasgupta,et al.  PAC Generalization Bounds for Co-training , 2001, NIPS.

[7]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[8]  Tao Li,et al.  Semisupervised learning from different information sources , 2005, Knowledge and Information Systems.

[9]  Ricardo Vilalta,et al.  A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[11]  Giorgio Valentini,et al.  Supervised and Unsupervised Ensemble Methods and their Applications , 2008 .

[12]  Philip S. Yu,et al.  Effective estimation of posterior probabilities: explaining the accuracy of randomized decision tree approaches , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[13]  Anil K. Jain,et al.  Clustering ensembles: models of consensus and weak partitions , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yiming Yang,et al.  Learning Multiple Related Tasks using Latent Independent Component Analysis , 2005, NIPS.

[15]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[16]  Arindam Banerjee,et al.  Bayesian cluster ensembles , 2009, Stat. Anal. Data Min..

[17]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[18]  Yizhou Sun,et al.  Heterogeneous source consensus learning via decision propagation and negotiation , 2009, KDD.

[19]  Lars Schmidt-Thieme,et al.  Ensembles of relational classifiers , 2008, Knowledge and Information Systems.

[20]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[21]  David G. Stork,et al.  Pattern Classification , 1973 .

[22]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[23]  Maria-Florina Balcan,et al.  Co-Training and Expansion: Towards Bridging Theory and Practice , 2004, NIPS.

[24]  Bogdan E. Popescu,et al.  PREDICTIVE LEARNING VIA RULE ENSEMBLES , 2008, 0811.1679.

[25]  Ana L. N. Fred,et al.  Analysis of consensus partition in cluster ensemble , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[26]  John Shawe-Taylor,et al.  Two view learning: SVM-2K, Theory and Practice , 2005, NIPS.

[27]  Philip S. Yu,et al.  Combining multiple clusterings by soft correspondence , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[28]  Joydeep Ghosh,et al.  CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS , 2008, MLMTA.

[29]  Koby Crammer,et al.  Learning from Multiple Sources , 2006, NIPS.

[30]  Fei Wang,et al.  Generalized Cluster Aggregation , 2009, IJCAI.

[31]  Qiang Yang,et al.  Semi-Supervised Learning with Very Few Labeled Training Examples , 2007, AAAI.

[32]  Bernard Zenko,et al.  Is Combining Classifiers Better than Selecting the Best One , 2002, ICML.

[33]  Ran El-Yaniv,et al.  Multi-way distributional clustering via pairwise interactions , 2005, ICML.

[34]  Ana L. N. Fred,et al.  Data clustering using evidence accumulation , 2002, Object recognition supported by user interaction for service robots.

[35]  Ian Davidson,et al.  When Efficient Model Averaging Out-Performs Boosting and Bagging , 2006, PKDD.

[36]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[37]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[38]  Chris H. Q. Ding,et al.  Weighted Consensus Clustering , 2008, SDM.

[39]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[40]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[41]  François Laviolette,et al.  A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning , 2008, NIPS.

[42]  William F. Punch,et al.  A Comparison of Resampling Methods for Clustering Ensembles , 2004, IC-AI.

[43]  Hui Xiong,et al.  Transfer learning from multiple source domains via consensus regularization , 2008, CIKM '08.

[44]  Leen Torenvliet,et al.  The value of agreement a new boosting algorithm , 2008, J. Comput. Syst. Sci..

[45]  Kurt Hornik,et al.  Voting-Merging: An Ensemble Method for Clustering , 2001, ICANN.

[46]  Kagan Tumer,et al.  Analysis of decision boundaries in linearly combined neural classifiers , 1996, Pattern Recognit..

[47]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[48]  Steven Skiena,et al.  Integrating microarray data by consensus clustering , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[49]  Susan T. Dumais,et al.  The Combination of Text Classifiers Using Reliability Indicators , 2016, Information Retrieval.

[50]  Vladimir Filkov,et al.  Consensus Clustering Algorithms: Comparison and Refinement , 2008, ALENEX.

[51]  Mikhail Belkin,et al.  A Co-Regularization Approach to Semi-supervised Learning with Multiple Views , 2005 .

[52]  Carlotta Domeniconi,et al.  Weighted cluster ensembles: Methods and analysis , 2009, TKDD.

[53]  Arindam Banerjee,et al.  Multi-way Clustering on Relation Graphs , 2007, SDM.

[54]  Hamidah Ibrahim,et al.  A Survey: Clustering Ensembles Techniques , 2009 .

[55]  Pedro M. Domingos.  Bayesian Averaging of Classifiers and the Overfitting Problem , 2000, ICML.

[56]  Inderjit S. Dhillon,et al.  Information-theoretic co-clustering , 2003, KDD '03.

[57]  Yizhou Sun,et al.  Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models , 2009, NIPS.

[58]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[59]  Jiawei Han,et al.  Knowledge transfer via multiple model local structure mapping , 2008, KDD.

[60]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[61]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[62]  Qiang Yang,et al.  Discovering Classification from Data of Multiple Sources , 2006, Data Mining and Knowledge Discovery.

[63]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[64]  Ulf Brefeld,et al.  Multi-view Discriminative Sequential Learning , 2005, ECML.

[65]  Klaus Nordhausen,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman , 2009 .

[66]  Ian Davidson,et al.  On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples , 2007, SDM.

[67]  Carla E. Brodley,et al.  Solving cluster ensemble problems by bipartite graph partitioning , 2004, ICML.

[68]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[69]  Vikas Singh,et al.  Ensemble Clustering using Semidefinite Programming , 2007, NIPS.

[70]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[71]  Ricardo Vilalta,et al.  Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.

[72]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[73]  Ben Taskar,et al.  Multi-View Learning over Structured and Non-Identical Outputs , 2008, UAI.

[74]  Marcus A. Maloof,et al.  Using additive expert ensembles to cope with concept drift , 2005, ICML.

[75]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[76]  Ludmila I. Kuncheva,et al.  Moderate diversity for better cluster ensembles , 2006, Inf. Fusion.

[77]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[78]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[79]  Jiawei Han,et al.  On Appropriate Assumptions to Mine Data Streams: Analysis and Practice , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[80]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[81]  Aristides Gionis,et al.  Clustering aggregation , 2005, 21st International Conference on Data Engineering (ICDE'05).

[82]  Ricardo Vilalta,et al.  Introduction to the Special Issue on Meta-Learning , 2004, Machine Learning.

[83]  Sandrine Dudoit,et al.  Bagging to Improve the Accuracy of A Clustering Procedure , 2003, Bioinform..

[84]  Chris H. Q. Ding,et al.  Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).