Regression with RBF Networks: Implementation and Techniques
Implementation of Nonlinear Function Regression with Code-Based Approaches
Least Squares Support Vector Machine Regression for Multidimensional Pyrim Data
Performing regression on multidimensional pyrim data using Least Squares Support Vector Machines (LS-SVM), requiring download of the LS-SVM toolbox for MATLAB/Python implementation.
MATLAB Implementation of RBF Neural Networks for Classification and Regression Tasks
MATLAB code for RBF neural networks supporting both classification and regression applications with detailed implementation insights
Gaussian Process Algorithms for Regression and Classification Applications
Implementation of Gaussian Process algorithms for regression and classification tasks, accompanying the book "Gaussian Process-Based Machine Learning." This distribution contains the latest v3.1 version updated on 2010-09-27, featuring enhanced kernel functions and optimization methods for improved predictive performance.
RBF Neural Network for Classification and Regression
RBF Neural Network implementation for classification and regression tasks, featuring practical code examples and key algorithmic explanations - highly recommended for download
Excel and MATLAB Hybrid Programming with Exlink for Regression and Simulation
Hybrid programming between Excel and MATLAB using Exlink to implement regression and simulation analysis, along with several interpolation calculation mini-programs including Lagrange interpolation, Newton interpolation, and cubic spline interpolation methods.
Relevant Vector Machine Toolbox Versions 1 and 2
Relevant Vector Machine Toolbox versions 1 and 2 supporting regression and classification tasks with executable demonstration files
Gradient Descent Regression Implementation in MATLAB
Implementing regression models using gradient descent optimization algorithm in MATLAB with code examples and parameter configuration
Kernel Statistical Learning Toolbox with Integrated KPCA, KDR, and KSRI - Dual Functionality for Classification and Regression
A classic kernel statistical learning toolbox integrating KPCA (Kernel Principal Component Analysis), KDR (Kernel Dimensionality Reduction), and KSRI (Kernel Statistical Regression Implementation) with dual classification and regression capabilities, featuring comprehensive data processing and visualization functions.
Easily Understandable SVM MATLAB Toolbox
User-Friendly Support Vector Machine Toolbox for MATLAB Implementation