Explore MATLAB source code curated for "回归算法" with clean implementations, documentation, and examples.
Implementation of Extreme Learning Machine (ELM) classification and regression algorithms using MATLAB, featuring high-speed training and strong generalization capabilities for large-scale datasets.
This program implements Support Vector Machine (SVM) regression algorithms, providing practical solutions with comprehensive code documentation for machine learning applications.
This implementation provides four distinct SVM toolboxes for classification and regression algorithms: 1) LS_SVMlab toolbox with Classification_LS_SVMlab.m for multi-class classification and Regression_LS_SVMlab.m for function fitting using Least Squares SVM approach; 2) OSU_SVM3.00 toolbox featuring Classification_OSU_SVM.m for multi-class classification; 3) stprtool SVM toolbox containing Classification_stprtool.m for multi-class classification tasks; 4) SVM_SteveGunn toolbox with Classification_SVM_SteveGunn.m for binary classification and Regression_SVM_SteveGunn.m for function fitting.
MATLAB source code implementations including Support Vector Classification (SVC_C, SVC_Nu), Support Vector Regression (SVR_Epsilon, SVR_Nu), and One-Class SVM algorithms with detailed parameter explanations and usage examples.
Comprehensive MATLAB programs for Support Vector Machine algorithms featuring: (1) Main_SVC_C.m - C-SVC multi-class classification with soft margin optimization, (2) Main_SVC_Nu.m - Nu-SVC binary classification using nu parameter control, (3) Main_SVM_One_Class.m - One-Class SVM for novelty detection, (4) Main_SVR_Epsilon.m - Epsilon-SVR regression with epsilon-intensive loss function, (5) Main_SVR_Nu.m - Nu-SVR regression utilizing nu parameter for regression control
MATLAB-based Extreme Learning Machine (ELM) algorithms for classification and regression tasks, featuring rapid training implementation and efficient matrix operations
Overview of Four Support Vector Machine Toolbox Classification and Regression Algorithms with Code Implementation Details
Classification and Regression Algorithms Using Four SVM Toolboxes
Classification and Regression Algorithms Using Four SVM Toolboxes