RLScore: Regularized Least-Squares Learners
Abstract
RLScore is a Python open source module for kernel based machine learning. The library provides implementations of several regularized least-squares (RLS) type of learners. RLS methods for regression and classification, ranking, greedy feature selection, multi-task and zero-shot learning, and unsupervised classification are included. Matrix algebra based computational short-cuts are used to ensure efficiency of both training and cross-validation. A simple API and extensive tutorials allow for easy use of RLScore.
Cite
Text
Pahikkala and Airola. "RLScore: Regularized Least-Squares Learners." Journal of Machine Learning Research, 2016.Markdown
[Pahikkala and Airola. "RLScore: Regularized Least-Squares Learners." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/pahikkala2016jmlr-rlscore/)BibTeX
@article{pahikkala2016jmlr-rlscore,
title = {{RLScore: Regularized Least-Squares Learners}},
author = {Pahikkala, Tapio and Airola, Antti},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-5},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/pahikkala2016jmlr-rlscore/}
}