Soft partitions lead to better learned ensembles
暂无分享,去创建一个
[1] Donald W. Bouldin,et al. A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[2] Nitesh V. Chawla,et al. Creating ensembles of classifiers , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[3] James M. Keller,et al. A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..
[4] HoTin Kam. The Random Subspace Method for Constructing Decision Forests , 1998 .
[5] Sankar K. Pal,et al. Fuzzy models for pattern recognition , 1992 .
[6] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[7] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[8] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[9] Paul S. Bradley,et al. Scaling Clustering Algorithms to Large Databases , 1998, KDD.
[10] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[11] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[12] Charles Elkan,et al. Scalability for clustering algorithms revisited , 2000, SKDD.
[13] Michael I. Jordan,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.
[14] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[15] James M. Keller,et al. The possibilistic C-means algorithm: insights and recommendations , 1996, IEEE Trans. Fuzzy Syst..