LibPAML: Open Source Machine Learning Library
Coming Soon in Q2 2016! Please sign-up for email announcements here
LibPAML (Library for Prediction and Machine Learning) is an open source library designed to help businesses leverage modern machine learning techniques in support of various business challenges, like recommendation engines or optimization problems. It is developed in C++ for high performance and runs on Linux and Mac OS.
- LibPAML is designed to be used in production environments and easy to use.
- LibPAML is a suite of powerful algorithms that can be combined to deliver practical recipes that solve business problems. These recipes can help you understand how machine learning algorithms work together to create effective solutions.
- LibPAML 0.7 Pangu [ download ] Coming soon in Q2/2015 Please sign-up for email announcements here
- AGPL v3
- Commercial License
SVD / SVD++
One-class MF (Matrix factorization for sparse 0/1 data set)
BPR-MF (Bayesian Personalized Ranking Matrix Factorization)
pLSA (Probabilistic Latent Semantic Analysis)
kNN (User / Item based k-Nearest Neighbors)
Time-aware SVD / SVD++ (in dev)
RBM (Restricted Boltzmann Machine) (in dev)
LASSO (in dev)
Elastic Net (in dev)