365 search results - page 16 / 73 » Generalized Chebyshev Kernels for Support Vector Classificat... |
COLT
1999
Springer
13 years 11 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...
ADMA
2005
Springer
13 years 9 months ago
2005
Springer
We develop the notion of normalized information distance (NID) [7] into a kernel distance suitable for use with a Support Vector Machine classifier, and demonstrate its use for an...
NIPS
2004
13 years 8 months ago
2004
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
DCC
2009
IEEE
14 years 8 months ago
2009
IEEE
Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among...
ICCV
2009
IEEE
13 years 5 months ago
2009
IEEE
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...