On the fusion of threshold classifiers for categorization and dimensionality reduction
暂无分享,去创建一个
[1] N. Sampas,et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling , 2000, Nature.
[2] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[3] Hans A. Kestler,et al. Specialized DNA Arrays for the Differentiation of Pancreatic Tumors , 2005, Clinical Cancer Research.
[4] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[5] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[6] Nello Cristianini,et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..
[7] François Laviolette,et al. Learning the set covering machine by bound minimization and margin-sparsity trade-off , 2009, Machine Learning.
[8] Rocco A. Servedio,et al. Toward Attribute Efficient Learning of Decision Lists and Parities , 2006, J. Mach. Learn. Res..
[9] David A. McAllester. PAC-Bayesian model averaging , 1999, COLT '99.
[10] Mario Marchand,et al. PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data , 2004, NIPS.
[11] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[12] J. Hanley,et al. The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.
[13] John Shawe-Taylor,et al. PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification , 2005, Machine Learning.
[14] L. Breiman. Arcing classifier (with discussion and a rejoinder by the author) , 1998 .
[15] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[16] Manfred K. Warmuth,et al. Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension , 1995, Machine Learning.
[17] Robert P. W. Duin,et al. Limits on the majority vote accuracy in classifier fusion , 2003, Pattern Analysis & Applications.
[18] John Shawe-Taylor,et al. The Set Covering Machine , 2003, J. Mach. Learn. Res..
[19] David Haussler,et al. Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework , 1988, Artif. Intell..
[20] Manfred K. Warmuth,et al. Relating Data Compression and Learnability , 2003 .
[21] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[22] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[23] T. Poggio,et al. Prediction of central nervous system embryonal tumour outcome based on gene expression , 2002, Nature.
[24] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[25] Wolfgang Huber,et al. A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks , 2004, Statistical applications in genetics and molecular biology.
[26] J. Langford. Tutorial on Practical Prediction Theory for Classification , 2005, J. Mach. Learn. Res..
[27] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[28] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[29] François Laviolette,et al. Margin-Sparsity Trade-Off for the Set Covering Machine , 2005, ECML.
[30] John Langford,et al. PAC-MDL Bounds , 2003, COLT.
[31] Marcel J. T. Reinders,et al. A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets , 2006, BMC Bioinformatics.
[32] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.