Explore MATLAB source code curated for "支持向量机" with clean implementations, documentation, and examples.
This MATLAB implementation of Support Vector Machine (SVM) provides a functional codebase for classification tasks, featuring data preprocessing, kernel selection, model training, and prediction capabilities.
This code provides comprehensive implementation details with thorough testing, ready for direct deployment in classification tasks using SVM algorithms.
This study explores image classification implementation using Libsvm, focusing on object classification with five fruit categories as research subjects. The workflow involves collecting image samples (primarily web-sourced), image preprocessing (e.g., resizing to uniform dimensions), feature vector extraction, Libsvm-based model training, and classification testing, with code-level descriptions of key algorithms and implementation approaches.
Application Background: Surrogate models (Kriging, RBF, etc.) - These toolboxes serve as universal MATLAB libraries for multidimensional function approximation and optimization methods. Key Technologies: MATLAB implementation featuring Radial Basis Functions, Kriging methods, Support Vector Machines, Support Vector Regression, Gaussian Process Metamodels, and Polynomial approximations with corresponding code algorithms.
Implementation of the Sequential Minimal Optimization (SMO) algorithm for Support Vector Machines (SVM). Includes MATLAB source code files, PDF documentation on SVM theory, and detailed Word documentation explaining the source code implementation.
Support Vector Machine (SVM), first proposed by Corinna Cortes and Vapnik in 1995, demonstrates unique advantages in solving small-sample, nonlinear, and high-dimensional pattern recognition problems. It can be extended to other machine learning tasks such as function fitting. In machine learning, SVM is a supervised learning model that analyzes data and recognizes patterns for classification and regression analysis. Key implementation aspects include kernel selection and margin optimization algorithms.
A MATLAB-implemented face recognition system based on Markov Model and Support Vector Machine with integrated face database. This robust implementation (non-original) demonstrates excellent performance with key features: database generation from training/test samples; face recognition rate calculation (96.5 ); specific image identification; real-time camera-based face recognition. The system utilizes probability transition matrices for feature extraction and SVM classification for pattern recognition.
Support Vector Machine implementation for image classification, segmentation, object detection, and recognition in artificial intelligence information processing systems
This program implements particle swarm optimization to tune least squares support vector machine parameters, featuring excellent performance with intelligent parameter selection algorithms and efficient computation workflows.
MATLAB-based implementation of Support Vector Machines (SVM) handling both linearly separable and non-separable cases, including nonlinear SVM with common kernel functions for enhanced classification and prediction analysis.