febeling/rb-libsvm
Ruby language bindings for LIBSVM
Relevance Vector Machine implementation using the scikit-learn API.
Appears on
Quick read
Latest capture 2026-08-22 03:09
0 paths
Agent instructions and tool configuration found in this repository.
No config files detected.
8 observed captures since 2026-05-22. Observed captures are shown by default.
Stars from first capture -2
Observed captures only
All tracked data
Observed snapshots
Observed snapshots
Nearest indexed repositories by embedding similarity.
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.