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JMLR
2006
89views more  JMLR 2006»
13 years 8 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
DSS
2008
186views more  DSS 2008»
13 years 8 months ago
A machine learning approach to web page filtering using content and structure analysis
As the Web continues to grow, it has become increasingly difficult to search for relevant information using traditional search engines. Topic-specific search engines provide an al...
Michael Chau, Hsinchun Chen
ICMLA
2010
13 years 6 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
ML
2002
ACM
146views Machine Learning» more  ML 2002»
13 years 8 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
MP
2006
137views more  MP 2006»
13 years 8 months ago
New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds
There are many applications related to singly linearly constrained quadratic programs subjected to upper and lower bounds. In this paper, a new algorithm based on secant approximat...
Yu-Hong Dai, Roger Fletcher