Classic MATLAB Implementation of HMM Algorithm
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Resource Overview
Classic MATLAB implementation of Hidden Markov Model (HMM) algorithm for pattern recognition and artificial intelligence applications, featuring complete training and recognition modules
Detailed Documentation
Pattern recognition represents a crucial task in the field of artificial intelligence. The Hidden Markov Model (HMM) algorithm serves as a classical approach widely applied in speech recognition, natural language processing, handwriting recognition, and numerous other domains. This implementation includes key functions for Baum-Welch parameter estimation and Viterbi decoding algorithms, enabling efficient state transition probability calculation and optimal path finding. To facilitate practical usage, we provide a comprehensive MATLAB program that implements the complete HMM framework, allowing researchers to conduct in-depth studies and explore various applications in this field. The code structure includes modular components for model initialization, forward-backward procedures, and sequence prediction, helping researchers better understand HMM principles and applications while establishing foundations for future investigations.
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