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» On Computing Functions with Uncertainty
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MFCS
2004
Springer
15 years 12 months ago
Approximating Boolean Functions by OBDDs
In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of approximatin...
Andre Gronemeier
PAMI
2008
139views more  PAMI 2008»
15 years 6 months ago
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
We consider the problem of learning a ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an -accurate approxim...
Vikas C. Raykar, Ramani Duraiswami, Balaji Krishna...
CI
2004
75views more  CI 2004»
15 years 6 months ago
Utility Functions for Ceteris Paribus Preferences
Although ceteris paribus preference statements concisely represent one natural class of preferences over outcomes or goals, many applications of such preferences require numeric u...
Michael McGeachie, Jon Doyle
CIKM
2007
Springer
16 years 24 days ago
Effective top-k computation in retrieving structured documents with term-proximity support
Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k com...
Mingjie Zhu, Shuming Shi, Mingjing Li, Ji-Rong Wen
BMCBI
2007
168views more  BMCBI 2007»
15 years 6 months ago
GOSim - an R-package for computation of information theoretic GO similarities between terms and gene products
Background: With the increased availability of high throughput data, such as DNA microarray data, researchers are capable of producing large amounts of biological data. During the...
Holger Fröhlich, Nora Speer, Annemarie Poustk...