Sequential Minimal Optimization (SMO) Algorithm for Support Vector Machine (SVM)

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Resource Overview

Implementation of the SMO algorithm for Support Vector Machine (SVM) with MATLAB source code files, SVM PDF documentation, and source code Word documentation. The project includes complete working examples demonstrating optimization steps and kernel function implementations.

Detailed Documentation

Support Vector Machine (SVM) is a widely-used machine learning algorithm that performs classification or regression tasks by maximizing the margin between data samples. This implementation focuses on the Sequential Minimal Optimization (SMO) algorithm, which efficiently solves the SVM quadratic programming problem by breaking it down into smallest possible subproblems. The package contains MATLAB source code files featuring key components such as kernel matrix computation, Lagrange multiplier optimization, and bias term calculation. Additionally, comprehensive PDF documentation explains the mathematical foundations while Word documentation provides detailed code walkthroughs. These resources offer practical implementation insights including handling of linear and non-linear kernels, optimization convergence criteria, and support vector selection mechanisms to facilitate better understanding and application of SVM algorithms.

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