Support Vector Machine (SVM) Computation Program

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Resource Overview

This program implements Support Vector Machine calculations with full functionality, personally validated through experimental testing. The implementation includes core SVM algorithms for classification tasks.

Detailed Documentation

This program is designed for computing Support Vector Machines (SVM), which are widely applied in pattern recognition and machine learning domains. Support Vector Machine represents a powerful classification algorithm that identifies optimal hyperplanes in high-dimensional spaces to separate data points from different classes. The implementation typically involves solving quadratic optimization problems to maximize margins between classes, utilizing kernel functions (linear, polynomial, or RBF) for handling non-linear separability. Through SVM computation, we can effectively address complex classification challenges. The program has been personally verified through experiments, ensuring its accuracy and reliability in practical applications. Key functions include data normalization, kernel selection, parameter optimization, and decision boundary calculation.

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