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.
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页数：5
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