A new intelligent pattern classifier based on deep-thinking

Abstract

A new intelligent pattern classifier based on the human being’s thinking logics is developed in this paper, aiming to approximate the optimal design process and avoid the matrix inverse computation in conventional classifier designs. It is seen that the proposed classifier has no parameters to be determined via mathematical optimization. Instead, it is built by using the correlation principles to construct the clusters at first. The middle-level feature vectors can then be extracted from the statistical information of the correlations between the input data and the ones in each pattern cluster. For accurate classification purpose, the advanced feature vectors are generated with the moments’ information of the middle-level feature vectors. After that, Bayesian inference is implemented to make decisions from the weighted sum of the advanced feature components. In addition, a real-time fine-tuning loop (layer) is designed to adaptively “widen” the border of each pattern clustering region such that the input data can be directly classified once they are located in one of the clustering regions. An experiment for the classification of the handwritten digit images from the MNIST database is performed to show the excellent performance and effectiveness of the proposed intelligent pattern classifier.