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ICML
2010
IEEE
14 years 11 months ago
Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
A sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable t...
Mingkui Tan, Li Wang, Ivor W. Tsang
CDC
2009
IEEE
159views Control Systems» more  CDC 2009»
15 years 2 months ago
A distributed machine learning framework
Abstract— A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed in...
Tansu Alpcan, Christian Bauckhage
RECOMB
2005
Springer
15 years 10 months ago
Learning Interpretable SVMs for Biological Sequence Classification
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
Christin Schäfer, Gunnar Rätsch, Sö...
ISCAS
2006
IEEE
116views Hardware» more  ISCAS 2006»
15 years 4 months ago
Signal processing for brain-computer interface: enhance feature extraction and classification
Abstract-In this paper we present a new scheme for brain imaginary movement invovles sophisticated spatial-temporalsignal processing and classification for electroencephalogram spe...
Haihong Zhang, Cuntai Guan, Yuanqing Li
ICML
2008
IEEE
15 years 11 months ago
Training SVM with indefinite kernels
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
Jianhui Chen, Jieping Ye