Unclassified region decreases the efficiency and performance of multi-class support vector machine. The proper selection of feature sub set reduced the unclassified region of multi-class support vector machine. Now a day’s multi-class classification are widely used in image classification. The feature selection or mapping of data one space to another space creates diversity of outlier and noise...

A progressive two-class decision classifier (pTCDC) was developed for hyperspectral data mapping to achieve maximum class separations between each class pair. In this paper, pTCDC is tested further by comparing it with other possible ways of converting multiclass to two-class classification including one-against-all and one-to-one methods used in implementing the newly developed support vector ...

Universum-support vector machine (U-SVM) is an elegant method for 2-class classification problem. It is systematically studied in this paper, including the existence and uniqueness of the primal problem as well as the relation between the solutions of primal problem and dual problem. We find that U-SVM uses 3-class classification approach to solve the 2-class classification problem. So we have ...

In nanopore sensing experiments, the properties of molecules are probed by the variation of ionic currents flowing through the nanopore. In this context, the electronic properties and the single-layer thickness of graphene constitute a major advantage for molecule characterization. Here we analyze the translocation pathway of the thioredoxin protein across a graphene nanopore, and the related i...

This paper describes MSVMpack, an open source software package dedicated to our generic model of multi-class support vector machine. All four multi-class support vector machines (M-SVMs) proposed so far in the literature appear as instances of this model. MSVMpack provides for them the first unified implementation and offers a convenient basis to develop other instances. This is also the first ...

In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class...

Support Vector Machines are learning paradigm originally developed on the basis of a binary classification problem with signed outputs ±1. The aim of this work is to give a probabilistic interpretation to the numerical output values into a multi-classification learning problem framework. For this purpose, a recent SV Machine, called `-SVCR, addressed to avoid the lose of information occurred in...

Support Vector Machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separabl...