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Mastering Support Vector Machines: Large-Margin Classifiers and the Kernel Trick

This lecture explores Support Vector Machines (SVM) as a powerful tool for classification tasks, focusing on large-margin linear classifiers in non-separable cases. Discover how SVMs handle complex data using the kernel trick to transform input space, enabling efficient classification of non-linearly separable datasets. Gain insights into the theoretical underpinnings and practical applications of SVMs, including key concepts, mathematical foundations, and examples that illustrate the strengths of this approach in machine learning.

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Mastering Support Vector Machines: Large-Margin Classifiers and the Kernel Trick

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    1. 1 Lecture 5 Support Vector Machines Large-margin linear classifier Non-separable case The Kernel trick

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