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International Journal of Quantum Chemistry

Volumn 115, Issue 16, 2015, Pages 1115-1128

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(8) Vu, Kevin a Snyder, John C b,c Li, Li a Rupp, Matthias d Chen, Brandon F a Khelif, Tarek f Müller, Klaus Robert b,e Burke, Kieron a

a UNIVERSITY OF CALIFORNIA (United States)

b TECHNISCHE UNIVERSITÄT BERLIN (Germany)

c MAX PLANCK INSTITUTE OF MICROSTRUCTURE PHYSICS (Germany)

d UNIVERSITY OF BASEL (Switzerland)

e Korea University (South Korea)

f UNIVERSITY OF CALIFORNIA (United States)

Author keywords

density functional theory; extreme behaviors; hyperparameters optimization; machine learning; noise free curve

Indexed keywords

CURVE FITTING; LEARNING SYSTEMS; MACHINE LEARNING; RANDOM ERRORS; REGRESSION ANALYSIS;

DENSITY FUNCTIONALS; EXTREME BEHAVIORS; FREE CURVES; HYPERPARAMETERS; KERNEL RIDGE REGRESSIONS; LARGE LENGTH-SCALE; RANDOM SAMPLING;

DENSITY FUNCTIONAL THEORY;

EID: 84936800623 PISSN: 00207608 EISSN: 1097461X Source Type: Journal
DOI: 10.1002/qua.24939 Document Type: Article

Times cited : (108)

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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.