Training and retraining of neural network trees

In machine learning, symbolic approaches usually yield comprehensible results without free parameters for further (incremental) retraining, On the other hand, non-symbolic (connectionist or neural network based) approaches usually yield black-boxes which are difficult to understand and reuse. The goal of this study is to propose a machine learner that is both incrementally retrainable and comprehensible through integration of decision trees and neural networks. In this paper, we introduce a kind of neural network trees (NNTrees), propose algorithms for their training and retraining, and verify the efficiency of the algorithms through experiments with a digit recognition problem.