New Support Vector Algorithms

New Support Vector Algorithms

We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter nu lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter epsilon in the regres...

New Clustering Algorithms for the Support Vector

7 This study presents two new clustering algorithms for partition of data sam8 ples for the Support Vector Machine (SVM) based hierarchical classification. 9 A divisive (top-down) approach is considered in which a set of classes is 10 automatically separated into two smaller groups at each node of the hierar11 chy. The first algorithm splits the data samples based on a variation of the 12 Norma...

Classifying LEP Data with Support Vector Algorithms

We have studied the application of different classification algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classifiers such as Support Vector Machines has not yet been tested in this environment. We chose two different problems to compare the performance of a Support Vect...

Support vector algorithms as regularization networks

Many recently proposed learning algorithms are clearly inspired by Suppport Vector Machines. Some of them were developed while trying to simplify the quadratic programming problem that has to be solved in training SVMs. Some others have been proposed to solve problems other than binary classification (for example one-class SVM for novelty detection). Though indeed attractive, for most of the le...

Support Vector Machines and Kernel Algorithms