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Title: Active learning for high throughput screening
Authors: De Grave, Kurt ×
Ramon, Jan
De Raedt, Luc #
Issue Date: 2008
Publisher: Springer
Host Document: Lecture Notes in Computer Science vol:5255 pages:185-196
Conference: Discovery Science edition:2008 location:Budapest, Hungary date:13-16 October 2008
Abstract: An important task in many scientific and engineering disciplines is to set up experiments with the goal of finding the best instances (substances, compositions, designs) as evaluated on an unknown target function using limited resources. We study this problem using machine learning principles, and introduce the novel task of active k-optimization.
The problem consists of approximating the k best instances with regard to an unknown function and the learner is active, that is, it can present a limited number of instances to an oracle for obtaining the target value. We also develop an algorithm based on Gaussian processes for tackling active k-optimization, and evaluate it on a challenging set of tasks related to structure-activity relationship prediction.
Description: This paper received the Carl Smith Student Award.
ISBN: 978-3-540-88410-1
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Laboratory of Virology and Chemotherapy (Rega Institute)
× corresponding author
# (joint) last author

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DS2008CRC-actfunopt.pdfPaper CRC Published 348KbAdobe PDFView/Open
actfunopt-nocomments-fixedsqrt.pdfcorrected version (sqrt was missing in lcb/optimism def.) Published 476KbAdobe PDFView/Open

 


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