DISTRIBUTED CLASSIFICATION LEARNING BASED ON NONLINEAR VECTOR SUPPORT MACHINES FOR SWITCHING NETWORKS

被引：6

作者：

Wang, Yinghui ^{[1
,2
]}

Lin, Peng ^{[1
,2
]}

Qin, Huashu ^{[3
]}

机构：

[1] Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China

[2] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing, Peoples R China

[3] Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing, Peoples R China

来源：

KYBERNETIKA | 2017年 / 53卷 / 04期

基金：

中国国家自然科学基金;

关键词：

nonlinear support vector machine; multi-agent system; distributed optimization; connectivity; MULTIAGENT SYSTEMS; OPTIMIZATION; CONSENSUS;

D O I：

10.14736/kyb-2017-4-0595

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we discuss the distributed design for binary classification based on the nonlinear support vector machine in a time-varying multi-agent network when the training data sets are distributeclly located and unavailable to all agents. In particular, the aim is to find a global large margin classifier and then enable each agent to classify any new input data into one of the two labels in the binary classification without sharing its all local data with other agents. We formulate the support vector machine problem into a distributed optimization problem in approximation and employ a distributed algorithm in a time-varying network to solve it. Our algorithm is a stochastic one with the high convergence rate and the low communication cost. With the jointly-connected connectivity condition, we analyze the consensus rate and the convergence rate of the given algorithm. Then some experimental results on various classification training data sets are also provided to illustrate the effectiveness of the given algorithm.

引用

页码：595 / 611

页数：17

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