MATLAB Machine Learning Code Implementation

This document provides well-structured MATLAB source code implementations for various machine learning algorithms. The collection includes robust multi-class Support Vector Machine (SVM) implementations that handle classification problems with multiple categories using one-vs-one or one-vs-all strategies. The pattern recognition module contains algorithms for clustering, classification, and dimensionality reduction, featuring key functions like k-means clustering and principal component analysis (PCA). The feature selection section comprises filter methods (such as correlation-based selection), wrapper methods (using forward/backward selection), and embedded methods (LASSO, ridge regression) to identify the most relevant features for model building. The regression implementations cover linear regression, polynomial regression, and support vector regression (SVR) with proper error handling and validation procedures. All code includes comprehensive comments, proper data preprocessing steps, and cross-validation techniques to ensure model reliability. Each algorithm is implemented with MATLAB's built-in functions and custom code where necessary, providing clear examples of parameter tuning and performance evaluation using metrics like accuracy, precision, recall, and mean squared error. These implementations serve as practical educational tools and ready-to-use solutions for real-world machine learning applications across various domains including image processing, signal analysis, and predictive modeling.