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Learn about advanced concepts in support vector machines (SVMs) in this comprehensive lecture. Explore soft margins, slack variables, and penalty terms to handle non-linearly separable data. Discover how to extend SVMs to multi-class problems using pairwise comparison and continuous ranking techniques. Gain insights into the practical applications of SVMs for complex classification tasks.
