Visualized Nonlinear Support Vector Machine for Multi-Class Classification
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In this document, I am sharing a visualized nonlinear Support Vector Machine (SVM) multi-classification source code that is highly practical and accessible for learning. The implementation includes kernel methods (like RBF or polynomial kernels) for handling nonlinear separability and employs one-vs-one or one-vs-rest strategies for multi-class classification. This code can help you solve classification problems while providing visualizations of decision boundaries and support vectors for better data analysis. You can modify and customize the code according to your needs—such as adjusting hyperparameters (C, gamma) or integrating different kernels—to adapt to various application scenarios. Beyond basic usage, the code structure allows extensions like cross-validation or grid search for model optimization. I hope this resource proves useful and inspires further exploration and research in machine learning.
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