Budget semi-supervised learning

Zhou Zhi-Hua*, Kwok Po NG, She Qiao-Qiao, Jiang Yuan

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

In this paper we propose to study budget semi-supervised learning,i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/or process all available unlabeled data. This setting is with practical importance because in most real scenarios although there may exist abundant unlabeled data, the computational resource that can be used is generally not unlimited. Effective budget semi-supervised learning algorithms should be able to adjust behaviors considering the given resource budget. Roughly,the more resource, the more exploitation on unlabeled data. As an example, in this paper we show that this is achievable by a simple yet effective method.

Original languageEnglish
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages588-595
Number of pages8
DOIs
Publication statusPublished - 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: 27 Apr 200930 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/TerritoryThailand
CityBangkok
Period27/04/0930/04/09

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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