Radial Basis Function Network for Multitask Pattern Recognition

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.