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Relevance Vector Machine implementation using the scikit-learn API.
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Ruby language bindings for LIBSVM
Python package for Bayesian Machine Learning with scikit-learn API
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, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
ThunderSVM: A Fast SVM Library on GPUs and CPUs
A general classifier module to allow Bayesian and other types of classifications. A fork of cardmagic/classifier.
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