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    ICML
    2009
    IEEE
    144views Machine Learning» more  ICML 2009»
    A Bayesian approach to protein model quality assessment
    15 years 10 months ago
    A Bayesian approach to protein model quality assessment
    Download  www.cs.cmu.edu
    Given multiple possible models b1, b2, . . . bn for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their qua...
    Hetunandan Kamisetty, Christopher James Langmead
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    NIPS
    2001
    185views Information Technology» more  NIPS 2001»
    Probabilistic principles in unsupervised learning of visual structure: human data and a model
    14 years 11 months ago
    Probabilistic principles in unsupervised learning of visual structure: human data and a model
    Download  www.physics.brown.edu
    To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
    Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...
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    TNN
    2008
    178views more  TNN 2008»
    IMORL: Incremental Multiple-Object Recognition and Localization
    14 years 9 months ago
    IMORL: Incremental Multiple-Object Recognition and Localization
    Download  www.csse.monash.edu.au
    This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
    Haibo He, Sheng Chen
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    GECCO
    2009
    Springer
    204views Optimization» more  GECCO 2009»
    Combined structure and motion extraction from visual data using evolutionary active learning
    15 years 2 months ago
    Combined structure and motion extraction from visual data using evolutionary active learning
    Download  www.cs.uvm.edu
    We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
    Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
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    BIOINFORMATICS
    2006
    105views more  BIOINFORMATICS 2006»
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    14 years 9 months ago
    Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure
    Download  noble.gs.washington.edu
    Darrin P. Lewis, Tony Jebara, William Stafford Nob...
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