709 search results - page 21 / 142 » Dynamically Adapting Kernels in Support Vector Machines |
IJCNN
2008
IEEE
14 years 2 months ago
2008
IEEE
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
IWANN
2005
Springer
14 years 1 months ago
2005
Springer
Based on the Nystr¨om approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large sc...
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 ...
NIPS
2004
13 years 9 months ago
2004
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. I...
ICPR
2004
IEEE
14 years 9 months ago
2004
IEEE
In this study, a support vector machine (SVM) classifies real world data of cytogenetic signals measured from fluorescence in-situ hybridization (FISH) images in order to diagnose...