Cognitively motivated learning of categorical data with Modeling Fields Theory

A cognitively inspired framework referred to as Modeling Fields Theory (MFT) is utilized as the basic methodology for learning categorical data, represented by large binary vectors. The presented solution, referred to as accelerated MAP, allows simultaneous learning and selection of the number of models. The key element of accelerated MAP is a steady increase of the regularization penalty combined with gradual decrease of the model vagueness. The operation of this algorithm on real world data is illustrated by applying the algorithm to a text categorization problem. The relationship between the described algorithm and the vague-to-crisp process logic and the dynamical system approach to cognition are discussed.