MATLAB Toolkit for Support Vector Machines (SVM) and Kernel Functions
A comprehensive MATLAB program collection for implementing Support Vector Machines (SVM) and various kernel functions with practical code examples
A Feature Selection Method for SVM Leveraging Genetic Algorithm Optimization
A genetic algorithm-based feature selection approach for SVM that reduces feature dimensionality, computational time, and potentially enhances model accuracy through optimal feature subset identification.
Optimization of Parameters C and γ in Support Vector Machines
Optimizing SVM hyperparameters C and gamma with cross-validation and grid search techniques
Multiple Kernel Learning: A Linear Combination Model for Kernel Fusion with Computational Efficiency Enhancements
Multiple kernel learning integrates multiple kernels through linear combination, often hindered by slow optimization due to explicit kernel computations. We propose explicit approximation of kernel mapping functions in finite-dimensional spaces, employing dual coordinate descent for SVM optimization with group Lasso regularization for kernel weights.
Fundamental Methods of Pattern Recognition
MATLAB source code implementations for fundamental pattern recognition algorithms including Least Squares, SVM, Neural Networks, K-Nearest Neighbors (KNN), Editing Methods, Feature Selection, and Feature Transformation techniques.
Discriminant Analysis using MATLAB's fitcsvm Function with Wine Dataset
Support Vector Machine (SVM) Discriminant Analysis implementation using MATLAB's built-in fitcsvm function, demonstrated on the Wine recognition dataset
Least Squares Support Vector Machine Toolbox with Implementation Insights
A comprehensive Least Squares Support Vector Machine (LS-SVM) toolbox providing robust implementations for classification tasks with detailed algorithmic explanations and MATLAB code integration examples.
Neural Network ELM Algorithm
The ELM algorithm for neural networks demonstrates faster performance than traditional BP and SVM methods while maintaining high accuracy. Implemented in MATLAB, this version includes modifications to support diverse functions and automatically generates classification matrices during data processing. The implementation features optimized matrix operations for hidden layer computation and efficient weight calculation algorithms.
HoG SVM Method for Face Recognition
Explore the HoG SVM face recognition approach with code implementation insights - valuable for researchers studying facial recognition algorithms and their practical applications.
MATLAB Code Implementation for SVM Classification
MATLAB SVM classification program compatible with versions 7.0 and above, featuring support vector machine algorithm implementation