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Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non- parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, inclusion of a variety of different classification and regression scenarios, and full flexibility for experts.
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