FEST, short for Fast Ensembles of Sparse Trees, is a piece of software for learning various types of decision tree committees from high dimensional sparse data.
- hector (https://github.com/xlvector/hector)
Golang machine learning lib. Currently, it can be used to solve binary classification problems.
- libfm (http://www.libfm.org/)
Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering.
- liblinear (http://www.csie.ntu.edu.tw/~cjlin/liblinear/)
LIBLINEAR is a linear classifier for data with millions of instances and features.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
- vowpal_wabbit (https://github.com/JohnLangford/vowpal_wabbit)
The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research.
- xgboot (https://github.com/tqchen/xgboost)
An optimized general purpose gradient boosting (tree) library.
