MATLAB Code Implementation for SVM Classifier
An SVM classifier designed for speech signal classification and recognition applications, featuring comprehensive content with selective implementation guidance for developers.
SVM Classifier for Multi-Class Image Classification
Implementing Support Vector Machine (SVM) classifier for categorizing various image types using feature extraction and machine learning techniques
Facial Recognition System Using Gabor Wavelet Transform, PCA Dimensionality Reduction, and SVM Multi-Class Classification
This MATLAB program implements a comprehensive facial recognition pipeline that first processes face images using Gabor wavelet transformation for feature extraction, then applies PCA (Principal Component Analysis) for dimensionality reduction, and finally employs an SVM (Support Vector Machine) classifier for multi-class recognition. The implementation supports the complete ORL face database and utilizes the libsvm toolbox (version: libsvm-mat-2.89-3[FarutoUltimate3.0]). Key implementation details include Gabor filter parameter configuration, PCA eigenvector computation, and SVM kernel function selection for optimal multi-class classification performance.
SVM-Based Image Steganalysis Algorithm with Three-Domain Feature Fusion
An image steganalysis algorithm utilizing SVM classifier with feature fusion across three domains: DCT domain, DWT domain, and spatial domain, featuring code implementation insights.
SVM Classification Algorithm Implementation in MATLAB Code
Effective MATLAB source code for SVM classifier implementation with detailed technical explanations and machine learning applications
Training SVM Classifiers Using Extracted Feature Information
In the MATLAB environment, feature information extracted through various algorithms can be utilized as input for training Support Vector Machine (SVM) classifiers
MATLAB-Based Speech Recognition Program with MFCC and SVM Implementation
This example implements a speech recognition system in MATLAB using traditional MFCC feature extraction and popular SVM classifier, serving as an excellent baseline for comparative studies. The implementation includes feature extraction pipeline and classification algorithms suitable for benchmarking purposes.
An SVM Classifier for Pedestrian Detection
This is an SVM classifier designed for classifying training samples in pedestrian detection applications, featuring direct MATLAB implementation with built-in support for key machine learning functions.
SVM Classifier
SVM classifier designed for classifying multidimensional sample points, adaptable for different numbers of classes. Implements pattern recognition algorithms with configurable kernel functions and hyperparameter optimization.
MATLAB Implementation of SVM Classifier with Code Examples
Complete MATLAB implementation of Support Vector Machine (SVM) classifier including data preprocessing, model training, hyperparameter optimization, and performance evaluation