Feature selection for SVMs

Feature selection for SVMs (2000)

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by J. Weston , S. Mukherjee , O. Chapelle , M. Pontil , T. Poggio , V. Vapnik

Venue:	Advances in Neural Information Processing Systems 13
Citations:	163 - 14 self

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@INPROCEEDINGS{Weston00featureselection,
    author = {J. Weston and S. Mukherjee and O. Chapelle and M. Pontil and T. Poggio and V. Vapnik},
    title = {Feature selection for SVMs},
    booktitle = {Advances in Neural Information Processing Systems 13},
    year = {2000},
    pages = {668--674},
    publisher = {MIT Press}
}

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Abstract

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data. 1

Citations

6696	The Nature of Statistical Learning Theory - Vapnik - 1995
812	Molecular classification of cancer: class discovery and class prediction by gene expression monitoring - Golub, Slonim, et al. - 1999
775	G: Wrappers for Feature Subset Selection - Kohavi, John - 1996
521	Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96: 6745–6750 Balbin M and Lopez-Otin C - Alon, Barkai, et al. - 1999
474	Gene selection for cancer classification using support vector machines - Guyon, Weston, et al. - 2002
340	Selection of relevant features and examples in machine learning - Blum, Langley - 1997
216	A General Framework for Object Detection - Papageorgiou, Oren, et al. - 1998
185	Pedestrian detection using wavelet templates - Oren, Papageorgiou, et al. - 1997
144	Feature selection via concave minimization and support vector machines - Bradley, Mangasarian - 1998
40	Support vector machine classification of microarray data - Mukherjee, Tamayo, et al. - 1998
35	Feature selection and dualities in maximum entropy discrimination - Jebara, Jaakkola - 2000
25	Choosing kernel parameters for support vector machines - CHAPELLE, VAPNIK, et al. - 2001
19	Image representations for object detection using kernel classi ers - Evgeniou, Pontil, et al. - 2000
5	Modeling high-dimensional discrete data with multi-layer neural networks - Bengio, Bengio - 2000
1	Sparse representation of multiple signals - Evgeniou, Poggio - 1997