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IBPRIA
2009
Springer
15 years 8 months ago
Inference and Learning for Active Sensing, Experimental Design and Control
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Exa...
Hendrik Kück, Matthew Hoffman, Arnaud Doucet,...
BMCBI
2006
101views more  BMCBI 2006»
15 years 3 months ago
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validatio...
Tim Van den Bulcke, Koen Van Leemput, Bart Naudts,...
ICCAD
2008
IEEE
107views Hardware» more  ICCAD 2008»
15 years 9 months ago
Importance sampled circuit learning ensembles for robust analog IC design
This paper presents ISCLEs, a novel and robust analog design method that promises to scale with Moore’s Law, by doing boosting-style importance sampling on digital-sized circuit...
Peng Gao, Trent McConaghy, Georges G. E. Gielen
OZCHI
2006
ACM
15 years 9 months ago
Learning from interactive museum installations about interaction design for public settings
This paper reports on the evaluation of a digitallyaugmented exhibition on the history of modern media. We discuss visitors’ interaction with installations and corresponding int...
Eva Hornecker, Matthias Stifter
ATAL
2008
Springer
15 years 5 months ago
Automated design of scoring rules by learning from examples
Scoring rules are a broad and concisely-representable class of voting rules which includes, for example, Plurality and Borda. Our main result asserts that the class of scoring rul...
Ariel D. Procaccia, Aviv Zohar, Jeffrey S. Rosensc...