NDLI: Radial Basis Function Network for Multitask Pattern Recognition

Content Provider	Springer Nature Link
Author	Nishikawa, Hitoshi Ozawa, Seiichi
Copyright Year	2011
Abstract	In this paper, a novel type of radial basis function network is proposed for multitask pattern recognition. We assume that recognition tasks are switched sequentially without notice to a learner and they have relatedness to some extent. We further assume that training data are given to learn one by one and they are discarded after learning. To learn a recognition system incrementally in such a multitask environment, we propose Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR). There are five distinguished functions in RAN-MTPR: one-pass incremental learning, task change detection, task categorization, knowledge consolidation, and knowledge transfer. The first three functions enable RAN-MTPR not only to acquire and accumulate knowledge of tasks stably but also to allocate classes to appropriate tasks unless task labels are not explicitly given. The fourth function enables RAN-MTPR to recover the failure in task categorization by minimizing the conflict in class allocation to tasks. The fifth function, knowledge transfer from one task to another, is realized by sharing the internal representation of a hidden layer with different tasks and by transferring class information of the most related task to a new task. The experimental results show that the recognition performance of RAN-MTPR is enhanced by introducing the two types of knowledge transfer and the consolidation works well to reduce the failure in task change detection and task categorization if the RBF width is properly set.
Starting Page	283
Ending Page	299
Page Count	17
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	33
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2011-05-07
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Radial basis function network Multitask learning Incremental learning Pattern recognition Knowledge transfer Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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