Support Vector Machines (SVMs) with linear kernels are powerful tools for binary classification tasks. They aim to find a separating hyperplane that maximizes the margin between classes, focusing on support vectors closest to the decision boundary. The formulation involves optimizing a quadratic pro

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Chunking with Support Vector Machines involves identifying proper chunks from a sequence of tokens and classifying them into grammatical classes using SVMs. This method utilizes chunk representations like IOB1, IOBES, and pairwise classification to achieve better performance in text chunking tasks.

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