Learning dynamic programming

Abstract

This dynamic programming includes a learning mode and an evaluation mode. In the learning mode, an accumulated DP path matrix is determined by a DP matching among a plurality of learning pattern vectors belonging to one category and a plurality of learning pattern vectors belonging to a similar category similar to the foregoing category and a standard pattern vector belonging to the foregoing category. Then path weights are calculated depending on the values of each element of the accumulated DP path matrix. In the evaluation mode, for performing a DP matching between evaluation pattern vectors and the standard pattern vector, a distance between elements of the above two pattern vectors is calculated with weighting by the above path weight. Thus, the optimum path in the evaluation mode is modified toward a frequent optimum path composed of a series of elements having high values in the foregoing accumulated DP path matrix.