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

共 14 条

[1] An experimental comparison of cross-validation techniques for estimating the area under the ROC curve [J].

Airola, Antti ;

Pahikkala, Tapio ;

Waegeman, Willem ;

De Baets, Bernard ;

Salakoski, Tapio .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2011, 55 (04) :1828-1844

[2] Cython: The Best of Both Worlds [J].

Behnel, Stefan ;

Bradshaw, Robert ;

Citro, Craig ;

Dalcin, Lisandro ;

Seljebotn, Dag Sverre ;

Smith, Kurt .

COMPUTING IN SCIENCE & ENGINEERING, 2011, 13 (02) :31-39

[3]

Jones E., 2001, SciPy: Open source scientific tools for Python

[4] Multi-label learning under feature extraction budgets [J].

Naula, Pekka ;

Airola, Antti ;

Salakoski, Tapio ;

Pahikkala, Tapio .

PATTERN RECOGNITION LETTERS, 2014, 40 :56-65

[5] Toward more realistic drug-target interaction predictions [J].

Pahikkala, Tapio ;

Airola, Antti ;

Pietila, Sami ;

Shakyawar, Sushil ;

Szwajda, Agnieszka ;

Tang, Jing ;

Aittokallio, Tero .

BRIEFINGS IN BIOINFORMATICS, 2015, 16 (02) :325-337

[6] Efficient regularized least-squares algorithms for conditional ranking on relational data [J].

Pahikkala, Tapio ;

Airola, Antti ;

Stock, Michiel ;

De Baets, Bernard ;

Waegeman, Willem .

MACHINE LEARNING, 2013, 93 (2-3) :321-356

[7] Unsupervised Multi-Class Regularized Least-Squares Classification [J].

Pahikkala, Tapio ;

Airola, Antti ;

Gieseke, Fabian ;

Kramer, Oliver .

12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2012), 2012, :585-594

[8] Efficient cross-validation for kernelized least-squares regression with sparse basis expansions [J].

Pahikkala, Tapio ;

Suominen, Hanna ;

Boberg, Jorma .

MACHINE LEARNING, 2012, 87 (03) :381-407

[9] An efficient algorithm for learning to rank from preference graphs [J].

Pahikkala, Tapio ;

Tsivtsivadze, Evgeni ;

Airola, Antti ;

Jarvinen, Jouni ;

Boberg, Jorma .

MACHINE LEARNING, 2009, 75 (01) :129-165

[10]

Pedregosa F, 2011, J MACH LEARN RES, V12, P2825

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