Explore MATLAB source code curated for "SVM" with clean implementations, documentation, and examples.
SVM Matlab GUI Visualization Interface provides intuitive graphical representation with detailed code explanations, featuring algorithm demonstrations and interactive controls implementation for both GUI development and SVM learning.
Hypersphere Support Vector Machine (HSVM), a one-class SVM algorithm that constructs hyperspheres for classification tasks, particularly effective for anomaly detection scenarios
High-quality research paper with accompanying source code. This study first reviews the application research status of load forecasting, summarizes the characteristics and influencing factors of load forecasting, categorizes common methods for short-term load forecasting, and analyzes the advantages and disadvantages of various methods. It then introduces the statistical learning theory as the theoretical foundation of Support Vector Machines (SVM) and explains SVM principles, deriving the SVM regression model. The paper employs a Least Squares Support Vector Machine (LSSVM) model, utilizing historical load data and meteorological data from Taizhou, Zhejiang Province to analyze various factors affecting predictions and summarize load variation patterns. The implementation includes preprocessing steps such as correcting "abnormal data" in historical load records and normalizing relevant factors for load forecasting. The study specifically addresses the significant impact of two key parameters in LSSVM models, which are currently determined empirically. The methodology incorporates parameter optimization using Particle Swarm Optimization (PSO) algorithm, where test set error serves as the criterion for parameter selection, demonstrating improved prediction accuracy through systematic parameter tuning.
Excellent MATLAB source code for Support Vector Machines, ready to use without path configuration, offering superior usability compared to libsvm!
Image segmentation using Support Vector Machine (SVM) with OTSU thresholding produces excellent results, directly generating binary images with enhanced feature extraction and classification capabilities
This article implements Dense SIFT for image feature extraction combined with Bag-of-Words (BoW) modeling. The BoW dictionary is constructed using only the training set since the test set is unavailable during development. The implementation covers BoW concept visualization, SVM classification with RBF kernel, and introduces a custom histogram intersection kernel based on research findings. The workflow includes feature encoding and demonstrates custom kernel integration in SVM.
This collection contains 30 practical MATLAB neural network case studies with executable programs, covering BP, RBF, SVM, SOM, Hopfield, LVQ, Elman, wavelet networks, and extending to optimization techniques like PSO (Particle Swarm Optimization), grey neural networks, fuzzy networks, probabilistic neural networks, and genetic algorithm implementations.
This MATLAB-based face recognition system utilizes PCA+SVM algorithms and includes executable source code with complete implementation details.
MATLAB SVM Toolbox - Practical Operations and Implementation for Support Vector Machines
Implementation and analysis of SVM kernel function selection and construction techniques in MATLAB environment, including built-in functions and custom kernel development approaches for optimal model performance.