Rank-r Updating Techniques for Fast Exact Cross-Validation
暂无分享,去创建一个
[1] Youfu Li,et al. Incremental support vector machine learning in the primal and applications , 2009, Neurocomputing.
[2] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[3] 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.
[4] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[5] H. Duan,et al. Decremental Learning Algorithms for Nonlinear Langrangian and Least Squares Support Vector Machines , 2007 .
[6] Okan K. Ersoy,et al. Recursive Update Algorithm for Least Squares Support Vector Machines , 2004, Neural Processing Letters.
[7] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[8] David R. Musicant,et al. Lagrangian Support Vector Machines , 2001, J. Mach. Learn. Res..
[9] Yuh-Jye Lee,et al. 2-SSVR : A Smooth Support Vector Machine for 2-insensitive Regression , 2004 .
[10] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[11] Gene H. Golub,et al. Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.
[12] William W. Hager,et al. Updating the Inverse of a Matrix , 1989, SIAM Rev..
[13] Su-Yun Huang,et al. Model selection for support vector machines via uniform design , 2007, Comput. Stat. Data Anal..
[14] O. Mangasarian,et al. Support vector machines in data mining , 2001 .
[15] Yuh-Jye Lee,et al. epsilon-SSVR: A Smooth Support Vector Machine for epsilon-Insensitive Regression , 2005, IEEE Trans. Knowl. Data Eng..
[16] Olvi L. Mangasarian,et al. Generalized Support Vector Machines , 1998 .
[17] Tong Zhang,et al. A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning , 2001, COLT/EuroCOLT.
[18] Yuh-Jye Lee,et al. SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..
[19] Gavin C. Cawley,et al. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.
[20] Klaus-Robert Müller,et al. Stopping conditions for exact computation of leave-one-out error in support vector machines , 2008, ICML '08.
[21] Claudio Gentile,et al. A Second-Order Perceptron Algorithm , 2002, SIAM J. Comput..
[22] Sylvain Arlot,et al. A survey of cross-validation procedures for model selection , 2009, 0907.4728.
[23] Johan A. K. Suykens,et al. Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.