Explore MATLAB source code curated for "SVM" with clean implementations, documentation, and examples.
MATLAB simulation program for water quality prediction implementing Support Vector Machines (SVM) and chaos theory, featuring comprehensive documentation with algorithm explanations and code structure details. This graduation project includes robust data preprocessing, phase space reconstruction, and predictive modeling components suitable for environmental research applications.
This project demonstrates the MATLAB implementation of Support Vector Machines (SVM), organized into five distinct folders representing different SVM variants with corresponding code examples and algorithm implementations.
Implementation of a classical pedestrian detection algorithm using HOG features and SVM classification, fully debugged and ready for deployment.
Regression Prediction Analysis with SVM Neural Networks - Shanghai Stock Exchange Opening Index Forecasting
QPSO Quantum Particle Swarm Algorithm, QPSO Optimized LSSVM, Support Vector Machine Implementation
This is a master's thesis titled "Research on SVM-based Algorithms for Petroleum Exploration" that implements the algorithm using MATLAB, providing detailed design approaches, comparative visualizations of different methods, and regional parameter adjustments. The implementation includes key SVM functions like data preprocessing, kernel selection, and hyperparameter optimization using MATLAB's Classification Learner app or custom scripts.
MATLAB source code implementing PCA for feature extraction and SVM for classification - Complete workflow with dimensionality reduction and machine learning algorithms
An SVM implementation example for categorizing different types of wines, complete with raw dataset containing various wine attributes and features.
Exploring Handwriting Recognition with SVM Implementation and Algorithm Applications
MATLAB Image Processing Toolbox implementing Principal Component Analysis (PCA), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) methodologies.