Support vector models are supervised models that are associated with learning algorithms. The algorithms analyze data and recognize patterns. The models are used for regression and classification analysis.

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Machine Learning can be supervised, unsupervised, semi-supervised, or reinforced. From the supervised algorithms, some of the common methods include Naive bayes classifiers and Support Vector Machines. Unsupervised learning includes k-means and hierarchical clustering.

Edward Tuck is known for co-authoring the book "Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond" with Bernhard Schölkopf and Alex J. Smola. He has also done research in the field of machine learning and support vector machines.