An Incremental Neural Network for Online Supervised Learning and Topology Learning
Youki Kamiya*, Shen Furao**, and Osamu Hasegawa**,***
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, R2-52, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
**Imaging Science and Engineering Lab., Tokyo Institute of Technology
***PRESTO, Japan Science and Technology Agency (JST)
A new self-organizing incremental network is designed for online supervised learning. During learning of the network, an adaptive similarity threshold is used to judge if new nodes are needed when online training data are introduced into the system. Nodes caused by noise are deleted to decrease the misclassification. The proposed network, which is robust to noisy training data, suits the following tasks: (1) online or even life-long supervised learning; (2) incremental learning, i.e., learning new information without destroying old learned information; (3) learning without any predefined optimal condition; (4) representing the topology structure of inputting online data; and (5) learning the number of nodes needed to represent every class. Experiments of artificial data and high-dimension real-world data show that the proposed method can achieve classification with a high recognition ratio, high speed, and low memory.
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