Incremental learning for multitask pattern recognition problems

Seiichi Ozawa, Asim Roy

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

2 Scopus citations

Abstract

This paper presents a learning model of multitask pattern recognition (MTPR) which is constructed by several neural classifiers, long-term memories, and the detector of task changes. In the MTPR problem, several multi-class classification tasks are sequentially given to the learning model without notifying their task categories. This implies that the learning model is supposed to detect task changes by itself and to utilize the knowledge on the previous tasks for learning of new tasks. In addition, the learning model must acquire knowledge of multiple tasks incrementally without unexpected forgetting under the condition that not only tasks but also training samples are sequentially given. The proposed model is evaluated for two artificial MTPR problem. In the experiments, we verify that the proposed model can acquire and accumulate task knowledge very stably and the speed of knowledge acquisition for new tasks is enhanced by transferring knowledge.

Original languageEnglish (US)
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages747-751
Number of pages5
DOIs
StatePublished - Dec 1 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Publication series

NameProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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  • Cite this

    Ozawa, S., & Roy, A. (2008). Incremental learning for multitask pattern recognition problems. In Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008 (pp. 747-751). [4725059] (Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008). https://doi.org/10.1109/ICMLA.2008.70