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    NIPS
    2008
    161views Information Technology» more  NIPS 2008»
    Stochastic Relational Models for Large-scale Dyadic Data using MCMC
    13 years 9 months ago
    Stochastic Relational Models for Large-scale Dyadic Data using MCMC
    Download  www.dbs.informatik.uni-muenchen.de
    Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
    Shenghuo Zhu, Kai Yu, Yihong Gong
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    KDD
    2005
    ACM
    73views Data Mining» more  KDD 2005»
    Using relational knowledge discovery to prevent securities fraud
    14 years 8 months ago
    Using relational knowledge discovery to prevent securities fraud
    Download  www.cs.purdue.edu
    We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world's largest...
    Özgür Simsek, David Jensen, Henry G. Gol...
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    SDM
    2007
    SIAM
    176views Data Mining» more  SDM 2007»
    Adaptive Concept Learning through Clustering and Aggregation of Relational Data
    13 years 9 months ago
    Adaptive Concept Learning through Clustering and Aggregation of Relational Data
    Download  siam.org
    We introduce a new approach for Clustering and Aggregating Relational Data (CARD). We assume that data is available in a relational form, where we only have information about the ...
    Hichem Frigui, Cheul Hwang
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    ICIP
    2005
    IEEE
    198views Image Processing» more  ICIP 2005»
    Curve segmentation using directional information, relation to pattern detection
    14 years 9 months ago
    Curve segmentation using directional information, relation to pattern detection
    Download  ericpichon.name
    We propose an extension of the conformal (or geodesic) active contour framework in which the conformal factor depends not only on the position of the curve but also on the directi...
    Eric Pichon, Allen Tannenbaum
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    AAMAS
    2005
    Springer
    152views Intelligent Agents» more  AAMAS 2005»
    Learning and Exploiting Relative Weaknesses of Opponent Agents
    13 years 7 months ago
    Learning and Exploiting Relative Weaknesses of Opponent Agents
    Download  www.cs.technion.ac.il
    Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of s...
    Shaul Markovitch, Ronit Reger
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