68 search results - page 5 / 14 » The Use of Bayesian Framework for Kernel Selection in Vector... |
COLT
1999
Springer
14 years 21 days 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...
PR
2010
13 years 6 months ago
2010
Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and ...
ICIP
2010
IEEE
13 years 6 months ago
2010
IEEE
Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
ARTMED
2007
13 years 8 months ago
2007
Objective: This study investigates the use of automated pattern recognition methods on magnetic resonance data with the ultimate goal to assist clinicians in the diagnosis of brai...
ICML
2008
IEEE
14 years 9 months ago
2008
IEEE
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...