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» Feature Selection for Support Vector Machines
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124
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DMIN
2009
132views Data Mining» more  DMIN 2009»
15 years 13 days ago
Understanding Support Vector Machine Classifications via a Recommender System-Like Approach
Support vector machines are a valuable tool for making classifications, but their black-box nature means that they lack the natural explanatory value that many other classifiers po...
David Barbella, Sami Benzaid, Janara M. Christense...
146
Voted
ICML
2003
IEEE
16 years 3 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
264
Voted
ISBRA
2011
Springer
14 years 6 months ago
Query-Adaptive Ranking with Support Vector Machines for Protein Homology Prediction
Abstract. Protein homology prediction is a crucial step in templatebased protein structure prediction. The functions that rank the proteins in a database according to their homolog...
Yan Fu, Rong Pan, Qiang Yang, Wen Gao
125
Voted
CGF
2005
252views more  CGF 2005»
15 years 2 months ago
Support Vector Machines for 3D Shape Processing
We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which...
Florian Steinke, Bernhard Schölkopf, Volker B...
142
Voted
COLT
1999
Springer
15 years 7 months ago
Covering Numbers for Support Vector Machines
—Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Until recently, the only bounds on th...
Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Ro...