Application of Gaussian Processes in Regression and Classification Problems
Implementation of Gaussian Processes with Detailed Annotations for Regression and Classification Problems, Including Code Examples
LIBSVM for Classification and Regression
LIBSVM is a simple, easy-to-use, and efficient software package developed by Professor Lin Chih-Jen and team at National Taiwan University for SVM-based pattern recognition and regression. It provides both precompiled Windows executables and source code for customization, cross-platform adaptation, and algorithm enhancement. The package simplifies parameter tuning with extensive default configurations that handle most practical scenarios while offering cross-validation capabilities. It supports C-SVM, ν-SVM, ε-SVR, ν-SVR models and multi-class classification using one-vs-one strategy, with optimized implementations for large-scale datasets.
Implementing Classification and Function Regression using Support Vector Machines (SVM)
Source code for classification and function regression using Support Vector Machines (SVM), including practical examples that can be easily adapted through straightforward modifications.
K-Nearest Neighbors Pattern Recognition Method
The K-Nearest Neighbors (KNN) algorithm can be utilized for both classification and regression tasks. By identifying k nearest neighbors of a sample and assigning the average of their attributes to that sample, we can predict the sample's properties. A more sophisticated approach involves assigning different weights to neighbors based on their distance, such as making weights proportional to distance, which improves prediction accuracy through weighted averaging.
Knowledge Reduction Algorithms Based on Rough Sets and Regression-Based Knowledge Reduction Representations
Exploration of rough set-based knowledge reduction algorithms and regression-based knowledge reduction representations with implementation insights
Support Vector Machine for Regression
Support Vector Machine implementation for regression tasks in MATLAB, featuring user-friendly functionality with comprehensive code examples
Latest Support Vector Machine Toolbox with Enhanced Features
The latest Support Vector Machine toolbox offers comprehensive functionality for streamlined machine learning workflows. Key capabilities include automated to-do list generation for project management, complete documentation support, Support Vector Regression (SVR) with epsilon-insensitive loss implementation, and intelligent model selection algorithms. Reference implementations include sequential minimal optimization (SMO) for efficient training.
MATLAB Demonstration Program for Support Vector Machine (SVM) Regression
This MATLAB demonstration showcases Support Vector Machine (SVM) Regression implementation, covering algorithm configuration, kernel selection, and practical applications with real-world datasets.
Easy-to-Understand SVM MATLAB Toolbox
An intuitive SVM MATLAB toolbox with classification and regression capabilities, featuring comprehensive examples and implementation guidance.
Regression and Prediction on 2D Function Datasets: Mexican Hat, Gabor, Friedman, and Polynomial
Implementation of Multilayer Perceptron (MLP) trained with Backpropagation, Radial Basis Function Network (RBF Network), and Support Vector Machine (SVM) for regression and prediction tasks on 2D function datasets including Mexican Hat, Gabor, Friedman, and Polynomial functions