Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening

We consider the problem of learning a sparse rule model , a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been compu...

Celý popis

Podrobná bibliografia

Vydané v:	IEEE Transactions on Pattern Analysis and Machine Intelligence Ročník 45; číslo 2; s. 2330 - 2343
Hlavní autori:	Kato, Hiroki, Hanada, Hiroyuki, Takeuchi, Ichiro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.02.2023 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Analytical models combinatorial algorithms convex programming Dictionaries knowledge representation formalisms and methods Learning Machine learning Numerical models Pattern analysis Prediction models Predictive models Random forests Regression tree analysis Regularization Screening
ISSN:	0162-8828, 1939-3539, 1939-3539, 2160-9292
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!