Combining Classifiers Using Correspondence Analysis (1998)

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by Christopher Merz
Citations:3 - 0 self

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44 Using Correspondence Analysis to Combine Classifiers – Christopher J. Merz, Sal Stolfo - 1998
151 Popular ensemble methods: an empirical study – David Opitz, Richard Maclin - 1999
24 A Principal Components Approach to Combining Regression Estimates – Christopher J. Merz, MICHAEL J. PAZZANI, Sal Stolfo - 1998
14 Cost Complexity-based Pruning of Ensemble Classifiers – Andreas L. Prodromidis, Salvatore J. Stolfo - 1999
42 An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning – Philip Kin-Wah Chan - 1996
4 Effective and Efficient Pruning of Meta-Classifiers in a Distributed Data Mining System – Andreas L. Prodromidis, Salvatore J. Stolfo, Philip K. Chan - 1999
9 Robust Combining of Disparate Classifiers through Order Statistics – Kagan Tumer, Joydeep Ghosh - 2001
71 Meta-Learning in Distributed Data Mining Systems: Issues and Approaches – Andreas L. Prodromidis, Philip K. Chan, Salvatore J. Stolfo - 2000
5 Performance and efficiency: Recent advances in supervised learning – Sheng Ma, Chuanyi Ji - 1999
52 Ensembling Neural Networks: Many Could Be Better Than All – Zhi-hua Zhou, Jianxin Wu, Wei Tang - 2002
71 Issues in Stacked Generalization – Kai Ming Ting, Ian H. Witten - 1999
22 Prototype Selection for Composite Nearest Neighbor Classifiers – David B. Skalak - 1997
102 Machine-Learning Research -- Four Current Directions – Thomas G. Dietterich
23 Knowledge Discovery Via Multiple Models – Pedro Domingos - 1998
3 Classifier Ensembles: Select Real-World Applications – Nikunj C. Oza, Kagan Tumer - 2008
56 Linear and Order Statistics Combiners for Pattern Classification – Kagan Tumer, Joydeep Ghosh - 1999
20 Dynamical Selection of Learning Algorithms – Christopher Merz - 1995
22 Cloud Classification Using Error-Correcting Output Codes – David W. Aha, Richard L. Bankert - 1996
29 Nearest neighbor classification from multiple feature subsets – Stephen D. Bay - 1999