Particle Swarm Optimization (PSO) for Support Vector Machine (SVM) Parameter Tuning
Using Particle Swarm Optimization (PSO) to optimize Support Vector Machine (SVM) parameters -c and -g with implementation insights
Support Vector Machine (SVM) Prediction Implementation
A well-structured MATLAB implementation for Support Vector Machine (SVM) prediction, featuring complete workflow from data loading to model evaluation
SVM EEG Signal Classification: Implementation and Algorithm Overview
SVM-based classification of EEG signals using supervised learning with hyperplane optimization for neural pattern recognition and brain-computer interface applications.
Image Segmentation Using Combined Spectral Clustering and SVM
A MATLAB-based image segmentation program integrating spectral clustering with SVM, implementing an effective spectral clustering algorithm for robust data classification tasks with enhanced feature extraction and cluster optimization capabilities.
SVM Training Implementation in MATLAB
svmTrain Function - Support Vector Machine Training in MATLAB with Code Implementation Details
SVM Toolbox Implementation in MATLAB
Comprehensive SVM toolbox featuring complete functionality sets, demonstration programs with code examples, and detailed documentation covering algorithm implementations
SVM-KMExample: MATLAB Implementation of Support Vector Machines with Comprehensive Code Examples
SVM-KMExample provides extensive MATLAB implementations of Support Vector Machines, featuring rich case studies with practical datasets, complete with ready-to-run code and algorithm explanations.
SVM for Image Classification Using Block-Based Feature Extraction
SVM for image classification using block-based feature extraction primarily focuses on determining image categories such as ancient architecture, water bodies, vegetation, etc. Implementation involves feature vector extraction through image partitioning and SVM model training for multi-class classification.
SVM-Based Regression Prediction Analysis: Predicting Shanghai Composite Index Opening Prices
Support Vector Machines (SVM) can be applied to both classification and regression prediction tasks. This case study demonstrates SVM implementation for regression analysis to predict stock market indices. Effective prediction of major indices provides crucial insights for observing overall market trends, making Shanghai Composite Index forecasting particularly valuable. Using daily opening prices from 1990.12.20 to 2009.08.19, the SVM regression model achieved impressive results: Mean Squared Error (MSE) = 1.95029e-005 and R-squared coefficient R = 99.9345%, indicating highly accurate fitting. Key implementation involves using SVM regression algorithms (like SVR) with appropriate kernel functions and parameter optimization.
Multi-Class SVM Classifier MATLAB Implementation
MATLAB code for multi-class Support Vector Machine classifier implementation using machine learning algorithms for accurate classification across multiple categories