LightTwinSVM: A Simple and Fast Implementation of Standard Twin Support Vector Machine Classifier

Directsvm: a Fast and Simple Support Vector Machine Perceptron

Single Chip Implementation of Support Vector Machine Based Bi - Classifier

This paper proposes the implementation of Support Vector Machine (SVM) on a single chip (dsPIC), which makes it suitable for standalone portable applications. The usual practice is to implement SVMs using general purpose computers, since its implementation demands fairly large amount of memory [1]. SVM implementation in this work uses Sequential Minimal Optimization (SMO) [1], with necessary mo...

Fault diagnosis in a distillation column using a support vector machine based classifier

Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...

A Simple, Fast Support Vector Machine Algorithm for Data Mining

Support Vector Machines (SVM) and kernel related methods have shown to build accurate models but the learning task usually needs a quadratic programming, so that the learning task for large datasets requires big memory capacity and a long time. A new incremental, parallel and distributed SVM algorithm using linear or non linear kernels proposed in this paper aims at classifying very large datas...

Twin support vector machine with Universum data

The Universum, which is defined as the sample not belonging to either class of the classification problem of interest, has been proved to be helpful in supervised learning. In this work, we designed a new Twin Support Vector Machine with Universum (called U-TSVM), which can utilize Universum data to improve the classification performance of TSVM. Unlike U-SVM, in U-TSVM, Universum data are loca...