Contradiction resolution with explicit and limited evaluation and its application to SOM

Abstract

In this paper, we improve contradiction resolution method. In contradiction resolution, a neuron is self-evaluated to fire without considering other neurons. On the other hand, a neuron is outer-evaluated by considering all neighboring neurons. We improve contradiction resolution by separating the results by self-evaluation from those by outer-evaluation and by limiting the number of winning neurons. The explicit separation is used to enhance contradiction between self and outer-evaluation. The reduction of the number of winning neurons is to focus on a limited number of neurons for extracting main characteristics of input patterns. We applied contradiction resolution to the Senate data. Experimental results confirmed that improved prediction was accompanied by improved visualization and interpretation performance.