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A multitask learning model for online pattern recognition

Ozawa, S. and Roy, A. and Roussinov, D. (2009) A multitask learning model for online pattern recognition. IEEE Transactions on Neural Networks, 20 (3). pp. 430-445. ISSN 1045-9227

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Abstract

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy'. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased.

Item type: Article
ID code: 19255
Keywords: automated task recognition, knowledge transfer, multitask learning, online learning, pattern recognition, Electronic computers. Computer science, Artificial Intelligence, Software, Computer Networks and Communications, Computer Science Applications
Subjects: Science > Mathematics > Electronic computers. Computer science
Department: Faculty of Science > Computer and Information Sciences
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 25 Oct 2010 09:50
    Last modified: 05 Sep 2014 03:20
    URI: http://strathprints.strath.ac.uk/id/eprint/19255

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