Abstract
In this paper, we propose a new information theoretic network growing algorithm. The new approach is called greedy information acquisition, because networks try to absorb as much information as possible in every stage of learning. In the first stage, two competitive units compete with each other by maximizing mutual information. In the successive stages, new competitive units are gradually added and information is maximized. Through greedy information maximization, different sets of important features in input patterns can be cumulatively discovered in successive stages. We applied our approach to a language classification problem. Experimental results confirmed that different features in input patterns are gradually discovered.
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