EXTRACTING FEATURE SUBSPACE FOR KERNEL BASED LINEAR PROGRAMMING SUPPORT VECTOR MACHINES

Extracting Feature Subspace for Kernel Based Linear Programming Support Vector Machines

We propose linear programming formulations of support vector machines (SVM). Unlike standard SVMs which use quadratic programs, our approach explores a fairly small dimensional subspace of a feature space to construct the nonlinear discriminator. This allows us to obtain the discriminator by solving a smaller sized linear program. We demonstrate that an orthonormal basis of the subspace can be ...

Kernel/feature Selection for Support Vector Machines

Support Vector Machines are classifiers with architectures determined by kernel functions. In these proceedings we propose a method for selecting the best SVM kernel for a given classification problem. Our method searches for the best kernel by remapping the data via a kernel variant of the classical Gram-Schmidt orthonormalization procedure then using Fisher’s linear discriminant on the remapp...

Linear programming support vector machines

Based on the analysis of the conclusions in the statistical learning theory, especially the VC dimension of linear functions, linear programming support vector machines (or SVMs) are presented including linear programming linear and nonlinear SVMs. In linear programming SVMs, in order to improve the speed of the training time, the bound of the VC dimension is loosened properly. Simulation resul...

Feature Selection Using Linear Support Vector Machines

Semiparametric Support Vector and Linear Programming Machines

Semiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms Support Vector machines and Linear Programming machines to this case and give experimental results for SV machines.