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.
updated at Aug. 18, 2024, 4:46 p.m.
LibXtract is a simple, portable, lightweight library of audio feature extraction functions.
updated at Oct. 6, 2024, 11:48 p.m.
ThunderGBM: Fast GBDTs and Random Forests on GPUs
updated at Nov. 6, 2024, 6:36 p.m.
ThunderSVM: A Fast SVM Library on GPUs and CPUs
updated at Nov. 10, 2024, 12:25 p.m.
An engine for high performance multi-agent environments with very large numbers of agents, along with a set of reference environments
updated at Nov. 14, 2024, 3:57 p.m.