Feasibility of Active Machine Learning for Multiclass Compound Classification

Journal of Chemical Information and Modeling

Volumn 56, Issue 1, 2016, Pages 12-20

  Lang, Tobias ^a   Flachsenberg, Florian ^a   Von Luxburg, Ulrike ^b   Rarey, Matthias ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

^b UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; MACHINE LEARNING;

ACTIVE LEARNING METHODS; ACTIVE MACHINE LEARNING; CLASSIFICATION MODELS; CLASSIFICATION TASKS; LEARNING TECHNIQUES; MACHINE LEARNING METHODS; MACHINE LEARNING TECHNIQUES; MULTI-CLASS CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

ANIMAL; DRUG DEVELOPMENT; FEASIBILITY STUDY; FEEDBACK SYSTEM; HUMAN; PROCEDURES; SUPERVISED MACHINE LEARNING;

ANIMALS; DRUG DISCOVERY; FEASIBILITY STUDIES; FEEDBACK; HUMANS; SUPERVISED MACHINE LEARNING;

EID: 84955472530     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.5b00332     Document Type: Article

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