Explore MATLAB source code curated for "Libsvm" with clean implementations, documentation, and examples.
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.
The k-fold cross-validation function commonly used in MATLAB's SVM implementations is often missing from the libsvm package and requires manual integration for comprehensive model evaluation.
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.
LibSVM Toolbox implemented in MATLAB environment with full VC interface compatibility! Visit my homepage for more details: http://huangbo929.blog.edu.cn
SVDD (Support Vector Data Description) algorithm in LIBSVM can be directly implemented and allows control over the number of remaining support vectors by adjusting gamma and C parameters through configuration file settings or function arguments.
Wavelet Packet Decomposition + Frequency Band Energy Analysis + Classification with LibSVM & Artificial Neural Networks
Support Vector Machine source code implementation using libsvm for classification, featuring parameter optimization techniques including kernel selection and C-value tuning
Comprehensive guide on using LIBSVM (a Support Vector Machine program created by Taiwan University professors), covering data preprocessing, data import, model training, and prediction with code implementation examples
The latest MATLAB interface for libsvm supports compilation on 64-bit operating systems, overcoming the 4GB memory process limitation for handling large-scale datasets through optimized memory management and parallel computing capabilities.
Implementation and methodology for cross-validation of libsvm parameters c and g with code integration approaches