ICANN
2007
Springer
13 years 9 months ago
2007
Springer
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
GFKL
2007
Springer
13 years 9 months ago
2007
Springer
In many classification applications, Support Vector Machines (SVMs) have proven to be highly performing and easy to handle classifiers with very good generalization abilities. Howe...
IJCNN
2000
IEEE
13 years 9 months ago
2000
IEEE
In the Support Vector Machines (SVM) framework, the positive-definite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant func...
ICANN
2001
Springer
13 years 9 months ago
2001
Springer
Sparse kernel regressors have become popular by applying the support vector method to regression problems. Although this approach has been shown to exhibit excellent generalization...
IWANN
2005
Springer
13 years 10 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...
ICNC
2005
Springer
13 years 10 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...
PAKDD
2007
ACM
13 years 11 months ago
2007
ACM
Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector...
MLDM
2007
Springer
13 years 11 months ago
2007
Springer
Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between feat...
ICANN
2007
Springer
13 years 11 months ago
2007
Springer
Support Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, th...
ICANN
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped tr...