Machine Learning

Support Vector Machines

Finds the boundary with the widest margin between classes, using kernels for non-linear data.

Rather than any separating line, an SVM looks for the one furthest from the nearest points of either class. Those nearest points are the support vectors, and they alone determine the boundary.

The kernel trick lets it draw curved boundaries by implicitly working in a higher-dimensional space without ever computing the coordinates. Excellent on small, wide datasets; slow and awkward on very large ones.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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