Analysis of Multiclass Support Vector Machines

Fuzzy Support Vector Machines Based on Density Estimation with Gaussian Mixture for Multiclass Problems

In this paper, we introduce new Fuzzy Support Vector Machines (FSVMs) for a multiclass classification. The suggested Fuzzy Support Vector Machines include the data distribution with the density estimated in a set of functions defined as Gaussian mixture. The proposed method gives more appropriate boundaries than the classical FSVM method. We demonstrate some examples which confirm our approach.

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

Multiclass Support Vector Machines Using Adaptive Directed Acyclic Graph

This paper presents a method of extending Support Vector Machines (SVMs) for dealing with multiclass problems. Motivated by the Decision Directed Acyclic Graph (DDAG), we propose the Adaptive DAG (ADAG): a modified structure of the DDAG that has a lower number of decision levels and reduces the dependency on the sequence of nodes. Thus, the ADAG improves the accuracy of the DDAG while maintaini...

Building Fuzzy Classifiers with Pairwise Multiclass Support Vector Machines

In this paper, we make a proposal to build a classifier with fuzzy outputs with multiclass Support Vector Machines (SVMs). This allows us to widen the applicability of this kind of powerful and soundly founded classifiers. We consider the pairwise multiclass version and investigate different alternatives for the aggregation process. A number of experiments have been carried out to establish the...