基于Universum的多视角全局和局部结构风险最小化模型  

Universum-based multi-view global and local structural risk minimization model

作　　者：朱昌明[1] 梅成就 周日贵 魏莱[1] 章夏芬[1] ZHU Changming;MEI Chengjiu;ZHOU Rigui;WEI Lai;ZHANG Xiafeng(Information Engineering College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学信息工程学院,上海201306

出　　处：《上海海事大学学报》2018年第3期97-102,共6页Journal of Shanghai Maritime University

基　　金：国家自然科学基金(61602296;61603245);上海市自然科学基金(16ZR1414500;16ZR1414400);上海市浦江人才计划(16PJ1403700)

摘　　要：为克服传统多视角分类器无法充分最小化结构风险的不足,提出基于Universum的多视角全局和局部结构风险最小化模型。该模型采用Universum学习,利用有标签样本生成大量包含分类信息的无标签样本,从而增加分类器性能。这些信息有利于最小化结构风险。通过在Mfeat、Reuters和Corel等3个多视角数据集上的试验可以发现,该模型可以提高多视角分类器的性能,并可以更好地应用到多视角数据集的分类问题中。In order to overcome the disadvantage of traditional multi-view classifiers that can not fully minimize structural risk,a Universum-based multi-view global and local structural risk minimization model is proposed.The model uses Universum learning,which uses labeled samples to generate a large number of unlabeled samples containing classification information so as to enhance the performances of classifiers.This information helps minimize structural risks.Experiments on three multi-view data sets,i.e.,Mfeat,Reuters and Corel,show that the model can improve the performance of multi-view classifiers and can be better applied to the classification of multi-view data sets.

关 键 词：Universum学习 多视角 结构风险 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]