A neural network model for sequential multitask pattern recognition problems

Hitoshi Nishikawa, Seiichi Ozawa, Asim Roy

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


In this paper, we propose a new multitask learning (MTL) model which can learn a series of multi-class pattern recognition prob- lems stably. The knowledge transfer in the proposed MTL model is implemented by the following mechanisms: (1) transfer by sharing the internal representation of RBFs and (2) transfer of the information on class subregions from the related tasks. The proposed model can detect task changes on its own based on the output errors even though no task information is given by the environment. It also learn training samples of different tasks that are given one after another. In the experiments, the recognition performance is evaluated for the eight MTPR problems which are defined from the four UCI data sets. The experimental results demonstrate that the proposed MTL model outperforms a single-task learning model in terms of the final classification accuracy. Furthermore, we show that the transfer of class subregion contributes to enhancing the generalization performance of a new task with less training samples.

Original languageEnglish (US)
Title of host publicationAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
Number of pages8
EditionPART 1
StatePublished - 2009
Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand
Duration: Nov 25 2008Nov 28 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th International Conference on Neuro-Information Processing, ICONIP 2008
Country/TerritoryNew Zealand

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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