Instead of a standard support vector machine (SVM) that classifies points by assigning them to one of two disjoint half-spaces, points are classified by assignment them to the closest of two parallel planes (in input or feature space) that are pushed apart as far as possible.Expand

Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the features (dimensions) as possible.Expand

IEEE Transactions on Pattern Analysis and Machine…

2006

TLDR

A new approach to support vector machine (SVM) classification is proposed wherein each of two data sets are proximal to one of two distinct planes that are not parallel to each other.Expand

Multisurface pattern separation is a mathematical method for distinguishing between elements of two pattern sets. Each element of the pattern sets is comprised of various scalar observations. In this… Expand

Optimality criteria form the foundations of mathematical programming both theoretically and computationally. In general, these criteria can be classified as either necessary or sufficient. Of course,… Expand

Smoothing methods, extensively used for solving important mathematical programming problems and applications, are applied here to generate an unconstrained smooth reformulation of the support vector machine for pattern classification using a completely arbitrary kernel.Expand

We have proposed a Reduced Support Vector Machine (RSVM) Algorithm 3.1 that uses a randomly selected subset of the data that is typically 10% or less of the original dataset for its explicit evaluation.Expand

A single linear programming formulation is proposed which generates a plane that of minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space.… Expand