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    SDM
    2007
    SIAM
    176views Data Mining» more  SDM 2007»
    Adaptive Concept Learning through Clustering and Aggregation of Relational Data
    15 years 5 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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    NIPS
    2004
    167views Information Technology» more  NIPS 2004»
    Surface Reconstruction using Learned Shape Models
    15 years 5 months ago
    Surface Reconstruction using Learned Shape Models
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    We consider the problem of geometrical surface reconstruction from one or several images using learned shape models. While humans can effortlessly retrieve 3D shape information, t...
    Jan Erik Solem, Fredrik Kahl
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    ENTCS
    2007
    119views more  ENTCS 2007»
    Interpolant Learning and Reuse in SAT-Based Model Checking
    15 years 4 months ago
    Interpolant Learning and Reuse in SAT-Based Model Checking
    Download  eprints.ecs.soton.ac.uk
    Bounded Model Checking (BMC) is one of the most paradigmatic practical applications of Boolean Satisfiability (SAT). The utilization of SAT in model checking has allowed signifi...
    João Marques-Silva
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    IJAMCIGI
    2010
    90views more  IJAMCIGI 2010»
    A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
    15 years 1 months ago
    A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
    Download  www.cs.nott.ac.uk
    Hyper-heuristics are identified as the methodologies that search the space generated by a finite set of low level heuristics for solving difficult problems. One of the iterative h...
    Ender Özcan, Mustafa Misir, Gabriela Ochoa, E...
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    ICCV
    2011
    IEEE
    218views Computer Vision» more  ICCV 2011»
    Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
    14 years 4 months ago
    Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
    Download  www.stat.ucla.edu
    We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
    George Papandreou, Alan L. Yuille
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