RLScore: Regularized Least-Squares Learners

被引：0

作者：

Pahikkala, Tapio ^{[1
]}

Airola, Antti ^{[1
]}

机构：

[1] 20014 Univ Turku, Dept Informat Technol, Turku, Finland

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2016年 / 17卷

基金：

芬兰科学院;

关键词：

cross-validation; feature selection; kernel methods; Kronecker product kernel; pair-input learning; !text type='python']python[!/text; regularized least-squares;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：5

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