単一例による学習とパターン認識

抄録

In this paper, I deal with learning and recognition in vision and utilize character recognition as an example. Although many researches are done on character recognition, they are mainly interested in applications and can not answer many questions in vision. Why can human beings recognize shifted, magnified, reduced, and/or distorted characters? Why do children frequently write a mirror image of character?.… Machine learning, especially similarity-based learning, requires many instances of a concept. However, human beings can recognize even rather distorted characters by learning from only a few instances. In this paper, I propose a new pattern recognition model. This model can recognize shifted, magnified, reduced, and/or distorted characters by learning from a single instance for each character. Moreover, it can answer some of questions in vision. An input pattern contains k_1*k_1 elements, and each element has any strength of stimulus. A structure of an input pattern is generated by using relations ; calculation of relations among elements, generation of segment nodes by using the relations, calculation of relations among the nodes, generation of more general nodes by using the relations, calculation of relations among the nodes, and so on. This structure is used to recognize the pattern and a part of the structure is memorized with weighted attributes by learning.