OAA-SVM-MS: A fast and efficient multi-class classification algorithm*

doi:10.1016/j.neucom.2021.04.115

OAA-SVM-MS: A fast and efficient multi-class classification algorithm*

2021-09-01

23

PRE

AI

DOI：10.1016/j.neucom.2021.04.115

Original

Original request for help

Save

Abstract

En 中文

This paper introduces the idea of learning uniformly ergodic Markov chain for one-against-all support vector machine (OAA-SVM) algorithm. We first obtain the generalization error of OAA-SVM with fast learning rate for uniformly ergodic Markov samples. We also propose a new OAA-SVM method with Markov sampling (OAA-SVM-MS). The experimental researches for benchmark repository confirm that the OAA-SVM-MS algorithm has significantly better performance in sampling and training total time, classification accuracy and the obtained classifier's sparsity compared to the classical OAA-SVM algo-rithm and other multi-class SVM algorithms. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

One-against-all

Multi-class

SVM

Generalization bound

OAA-SVM-MS: A fast and efficient multi-class classification algorithm*

Abstract

AI Summary

Journal

Organization