416 search results - page 33 / 84 » Nonlinear Feature Selection by Relevance Feature Vector Mach... |
142
Voted
COLT
1999
Springer
15 years 7 months ago
1999
Springer
—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...
115
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ICNC
2005
Springer
15 years 8 months ago
2005
Springer
The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimizat...
PKDD
2009
Springer
15 years 9 months ago
2009
Springer
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
133
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ICIAP
1999
ACM
15 years 6 months ago
1999
ACM
—Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which ...
131
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GECCO
2008
Springer
15 years 3 months ago
2008
Springer
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...