Pattern classifier capable of incremental learning

摘要

The invention provides a pattern classifier capable of incremental learning. Two attractive features of this pattern classifier are that the convergence of learning is guaranteed and training time can be remarkably reduced. The pattern classifier realizes incremental learning in three main steps. Firstly, a multiclass classification problem is divided into two-class classification subproblems, and each of these two-class classification subproblems is further divided into a number of linearly separable subproblems, each of which has only two training data belonging to two different classes. Secondly, complete learning of each of the linearly separable subproblems is performed in parallel. Finally, the solutions to the original multiclass problem emerged by simply combining the solutions of the linearly separable subproblems according to two module combinations laws, namely the minimization principle and the maximization principle, respectively. Since the module combination laws are completely independent of the structure of individual trained modules and their performance, to add new training data to previously trained pattern classifier can be realized efficiently.