Hidden Markov Models and Neural Networks for Pattern Recognition
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Resource Overview
Complete graduation project implementation featuring data collection, preprocessing, model training, and recognition systems. Includes practical implementations of Hidden Markov Models (HMM) and neural networks with comprehensive dataset handling. This reference-quality code demonstrates end-to-end machine learning pipeline development.
Detailed Documentation
This repository contains the complete implementation of my graduation project, serving as a valuable reference for pattern recognition systems. The project encompasses a full machine learning pipeline including data collection routines, data preprocessing methods (featuring normalization and feature extraction techniques), model training implementations, and recognition modules. The core algorithms include Hidden Markov Models (HMM) with Baum-Welch training for sequential data processing and neural networks utilizing backpropagation for pattern classification. This project represents the culmination of my academic journey, and I take pride in sharing this comprehensive implementation as both a technical resource and personal milestone.
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