Batch learning competitive associative net and its application to time series prediction
Shuichi Kurogi, Tomoya Ueno, Miho Sawa · 2005
A batch learning method for competitive associative net called CAN2 is presented and applied to time series prediction of the CATS benchmark (for competition on artificial time series). We have presented online learning methods for the CAN2 so far, which are basically for infinite number of training data. Provided that only a finite number of training data are given, however, the batch learning scheme seems more suitable. We here present a batch learning method to efficiently learn a finite number of data. We finally apply the present method to the time series prediction of the CATS benchmark.