Support vector machines with adaptive Lq penalty

[1]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[2]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[3]  Chong Gu,et al.  Soft Classification, a. k. a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Ana , 1993 .

[4]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[5]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[6]  Russell Greiner,et al.  Computational learning theory and natural learning systems , 1997 .

[7]  Paul S. Bradley,et al.  Feature Selection via Concave Minimization and Support Vector Machines , 1998, ICML.

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  Wenjiang J. Fu.  Penalized Regressions: The Bridge versus the Lasso , 1998 .

[10]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[11]  Nello Cristianini,et al.  Advances in Kernel Methods - Support Vector Learning , 1999 .

[12]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

[13]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[14]  G. Wahba.  Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV , 1999 .

[15]  Wenjiang J. Fu,et al.  Asymptotics for lasso-type estimators , 2000 .

[16]  Jianqing Fan,et al.  Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .

[17]  Jianqing Fan,et al.  Regularization of Wavelet Approximations , 2001 .

[18]  Koby Crammer,et al.  On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..

[19]  Robert Tibshirani,et al.  1-norm Support Vector Machines , 2003, NIPS.

[20]  Yi Lin.  Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .

[21]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[22]  Yi Lin,et al.  Support Vector Machines for Classification in Nonstandard Situations , 2002, Machine Learning.

[23]  Yi Lin,et al.  Support Vector Machines and the Bayes Rule in Classification , 2002, Data Mining and Knowledge Discovery.

[24]  Kazushi Ikeda,et al.  Geometrical Properties of Nu Support Vector Machines with Different Norms , 2005, Neural Computation.

[25]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[26]  Xiaotong Shen,et al.  MULTI-CATEGORY SUPPORT VECTOR MACHINES, FEATURE SELECTION AND SOLUTION PATH , 2006 .

[27]  Tong Tang,et al.  Proceedings of the European Symposium on Artificial Neural Networks , 2006 .