A neural network capable of learning and inference for visual pattern recognition

Abstract

In this paper, a neural network model which has logical inference capability as well as inductive learning capability for pattern recognition is presented. This model, Adaptive Inference Network (AINET), is a feedforward neural network consisting of two types of connections and can be trained by a backpropagation algorithm. The proposed model exhibits four major characteristics: logical inference ability, knowledge acquisition by learning, performance improvement by utilizing expert knowledge and result explanation capability. We first describe in this paper the network structure and behavior, the learning method, and other features of the proposed network. We then introduce a hybrid image recognition model using the neural network. The model consists of two stages: feature extraction and recognition. For the feature extraction stage, modular structure neural networks and conventional approaches are used together to extract the features more effectively. In the image recognition model, as a methodology of utilizng the knowledge from human experts, three forms of knowledge representations and a multistage learning technique are also presented. From the results of the typed and handwritten digit recognition experiment, the effectiveness of the proposed network on image recognition applications is evaluated.