MATLAB Implementation of Online Support Vector Machine (SVM)
- Login to Download
- 1 Credits
Resource Overview
An implementation of online SVM that enables dynamic modeling through selective sample retention and removal. This code requires SVM toolbox support and includes both the toolbox components and source code for the online SVM algorithm, featuring incremental learning capabilities and efficient memory management.
Detailed Documentation
This implementation demonstrates an online Support Vector Machine (SVM) approach that utilizes selective sample inclusion and exclusion strategies. The methodology enables dynamic modeling applications where SVM can adapt to streaming data (requires SVM toolbox support). The provided source code includes comprehensive SVM toolbox components alongside the core online SVM implementation, featuring key functions for incremental weight updates, kernel computations, and support vector maintenance. The algorithm employs a sliding window mechanism for sample management and implements efficient optimization routines for real-time model adjustments, making it suitable for research and practical applications in dynamic pattern recognition scenarios.
- Login to Download
- 1 Credits