Hidden Neural Network for Complex Pattern Recognition: A Comparison Study with Multi- Neural Network Based Approach

Abstract

When the feature space undergoes changes, owing to different operating and environmental conditions, multi-aspect classification is almost a necessity in order to maintain the performance of the pattern recognition system and improve robustness and reliability in decision making. This is an important issue being investigated in ANN research, in many cases, the problems can be solved more effectively by combining one or two other techniques rather than implementing ANN exclusively. New learning methods, especially multiple classifier systems, are now actively studied and applied in pattern recognition. So, the main goal of this paper is to propose two hybrid models and compare your performance in complex pattern recognition problem: speech recognition and biomedical diagnosis. This paper compare, the performance obtained with (1) Multi-network RBF/LVQ structure, we use involves Learning Vector Quantization (LVQ) as a competitive decision processor and Radial Basis Function (RBF) as a classifier. (2) Hybrid HMM/MLP model using a Multi Layer-Perceptron (MLP) to estimate the Hidden Markov Models (HMM) emission probabilities. For preclassification, the k-means clustering algorithm is proposed to obtain optimum information for the biomedical and speech training data for the proposed hybrid models.