Impartial competitive learning in multi-layered neural networks

The present paper aims to propose a new learning and interpretation method called "impartial competitive learning", meaning that all participants in a competition should be winners. Due to its importance, the impartiality is forced to be realised even by increasing the corresponding cost in terms of the strength of weights. For the first approximation, three types of impartial competition can be developed: componential, computational, and collective competition. In the componential competition, every weight should have an equal chance on average to win the competition. In the computational competition, all computational procedures should have an equal chance to be applied sequentially in learning. In collective computing for interpretation, all network configurations, obtained by learning, have an equal chance to participate in a process of interpretation, representing one of the most idealised forms of impartiality. The method was applied to a well-known second-language-learning data set. The intuitive conclusion, stressed in the specific science, could not be extracted by the conventional natural language processing methods, because they can deal only with word frequency. The present method tried to extract a main feature beyond the word frequency by competing connection weights and computational procedures impartially, followed by collective and impartial competition for interpretation.