848 search results - page 84 / 170 » Feature selection in a kernel space |
159
Voted
PAMI
2012
13 years 5 months ago
2012
—In this paper, we present a novel image classification system that is built around a core of trainable filter ensembles that we call Volterra kernel classifiers. Our system trea...
145
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...
110
Voted
JUCS
2007
15 years 2 months ago
2007
: A novel support vector machine method for classification is presented in this paper. A modified kernel function based on the similarity metric and Riemannian metric is applied ...
118
Voted
IDA
2002
Springer
15 years 2 months ago
2002
Springer
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
104
click to vote
SLSFS
2005
Springer
15 years 8 months ago
2005
Springer
We present a method which uses example pairs of equal or unequal class labels to select a subspace with near optimal metric properties in a kernel-induced Hilbert space. A represen...