SVM MATLAB Toolbox with GUI Interface
A highly functional SVM MATLAB toolbox featuring a comprehensive user interface, tested for reliability and ease of use
MATLAB Implementation of Support Vector Machine (SVM) Code
MATLAB program for Support Vector Machine (SVM) implementation, demonstrating the fundamental SVM architecture with core algorithm workflow
Easy-to-Understand SVM MATLAB Toolbox
An intuitive SVM MATLAB toolbox with classification and regression capabilities, featuring comprehensive examples and implementation guidance.
SVM Algorithm with Implementation Examples
Includes complete source code, datasets, and reference materials. Originally provided as classroom teaching resources for cluster analysis applications. Contains distinct learning and training modules suitable for SVM beginners. Features detailed algorithm explanations with code annotations covering key functions like data preprocessing, model training with kernel selection (linear/RBF), and prediction interfaces.
MATLAB SVM KM Toolbox Examples
Examples demonstrating the use of MATLAB SVM KM Toolbox for machine learning implementations
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
Commonly Used Data Mining Algorithms: ID3, K-means, FCM, SVM, and CART
Overview of five widely-used data mining algorithms - ID3, K-means, FCM, SVM, and CART - implemented using MATLAB with code implementation insights
Feature Extraction Using Convolutional Neural Networks Followed by SVM Classification
First, extract data features using convolutional neural networks, then perform classification using Support Vector Machines (SVM)
MATLAB-Based Plant Pest Detection System with GUI Interface and SVM Classification
This system requires the development of a GUI platform that enables users to open cucumber leaf images captured by electronic devices, perform image processing operations, conduct analytical assessments, and ultimately identify diseases affecting cucumber leaves [15]. The MATLAB-based system processes cucumber leaf images acquired from computer-connected cameras, stores processed images with disease annotations, and implements core functionalities including image processing, image enhancement, and program termination. Key GUI features include "Process Image," "Save Image," and "Exit" options. During image processing, the system incorporates reset functionality to restore original images for reprocessing, with all operations executed through sequential image processing steps. Error handling mechanisms address exceptional operational scenarios.
SVM for Time Series Forecasting with MATLAB Implementation
MATLAB-based SVM program for time series prediction featuring multiple forecasting models with detailed algorithm implementation