Pattern Recognition Assignment Programs
Self-implemented pattern recognition algorithms including Perceptron Algorithm, Multi-class Perceptron Algorithm, and K-means Clustering with detailed code implementation
Implementation of CART Algorithm in MATLAB
A MATLAB-based implementation of CART (Classification and Regression Trees) algorithm for pattern recognition tasks, supporting both classification and regression analysis. The package includes detailed documentation, sample routines, and code explanations covering key functions like tree building, node splitting using Gini impurity, and pruning techniques.
BP Artificial Neural Network Pattern Recognition Tutorial with Practical Examples
This tutorial provides comprehensive BP neural network examples for pattern recognition applications, featuring implementation guidance for facial recognition and gesture recognition systems with code structure explanations.
Hidden Semi-Markov Model: Implementation and Applications
The Hidden Semi-Markov Model enables computation through parameter substitution, suitable for pattern recognition, remaining useful life prediction, and other time-series analysis tasks. Implementation typically involves state duration modeling and forward-backward algorithm extensions.
Gaussian Models in Pattern Recognition
Gaussian Models in Pattern Recognition - A Practical MATLAB Implementation with Code Examples and Algorithm Explanation
MATLAB Code Implementation for Support Vector Machine (SVM)
SVM program code for pattern recognition and classification, applicable to image feature processing with enhanced algorithm implementation details.
Feature Extraction Using Principal Component Analysis
MATLAB source code for feature extraction through Principal Component Analysis in pattern recognition, including algorithm implementation and key function explanations
MATLAB Source Code for Pattern Recognition and Clustering Algorithms
MATLAB-implemented source files for pattern recognition and clustering, featuring comprehensive implementations of major clustering algorithms with configurable parameters and optimization options
Source Code for Pattern Recognition and Intelligent Computing Implementation in MATLAB
MATLAB source code implementation for pattern recognition and intelligent computing, featuring clustering analysis algorithms and handwritten digit classification systems with practical examples
LIBSVM for Classification and Regression
LIBSVM is a simple, easy-to-use, and efficient software package developed by Professor Lin Chih-Jen and team at National Taiwan University for SVM-based pattern recognition and regression. It provides both precompiled Windows executables and source code for customization, cross-platform adaptation, and algorithm enhancement. The package simplifies parameter tuning with extensive default configurations that handle most practical scenarios while offering cross-validation capabilities. It supports C-SVM, ν-SVM, ε-SVR, ν-SVR models and multi-class classification using one-vs-one strategy, with optimized implementations for large-scale datasets.