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2016 JMLR JMLR 2016

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. [abs] [ pdf ][ bib ] © JMLR 2016. (edit, beta)

🧭 Keyword Pioneer — regularized least-square
🐣 Hot Topic Early Bird — zero-shot learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Tapio Pahikkala , Antti Airola

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Regression Machine Learning > Learning Types > Zero-Shot Learning

Keywords

zero-shot learning multi-task learning feature selection kernel methods regularized least-square
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