Implementation and comparison of SVM-based Multi-Task Learning methods

Han Tai Shiao, Vladimir S Cherkassky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, data can be naturally separated into several groups, or tasks, and incorporating this information into learning may improve generalization. There are many Multi-Task Learning (MTL) techniques for classification recently proposed in machine learning. This paper focuses on analysis and comparison of the two recent SVM-based MTL techniques: regularized MTL (rMTL) and SVM+ based MTL (SVM+MTL). In particular, our analysis shows how these two methods can be implemented using standard SVM software. Further, we present extensive empirical comparisons between these two methods, which relates advantages/limitations of each method to statistical characteristics of the training data.

Original languageEnglish (US)
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - Aug 22 2012
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: Jun 10 2012Jun 15 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
CountryAustralia
CityBrisbane, QLD
Period6/10/126/15/12

Keywords

  • SVM-Plus (SVM+)
  • classification
  • land mine data
  • model selection
  • multi-task learning (MTL)
  • support vector machine

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