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    CVPR
    2004
    IEEE
    276views Computer Vision» more  CVPR 2004»
    A Graphical Model Framework for Coupling MRFs and Deformable Models
    14 years 9 months ago
    A Graphical Model Framework for Coupling MRFs and Deformable Models
    Download  cbim.rutgers.edu
    This paper proposes a new framework for image segmentation based on the integration of MRFs and deformable models using graphical models. We first construct a graphical model to r...
    Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas
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    NIPS
    1996
    84views Information Technology» more  NIPS 1996»
    Solving the Ill-Conditioning in Neural Network Learning
    13 years 9 months ago
    Solving the Ill-Conditioning in Neural Network Learning
    Download  www.robotic.dlr.de
    Abstract. In this paper we investigate the feed-forward learning problem. The well-known ill-conditioning which is present in most feed-forward learning problems is shown to be the...
    P. Patrick van der Smagt, Gerd Hirzinger
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    IJDAR
    2010
    169views more  IJDAR 2010»
    A Bayesian network for combining descriptors: application to symbol recognition
    13 years 6 months ago
    A Bayesian network for combining descriptors: application to symbol recognition
    Download  hal.archives-ouvertes.fr
    Inthispaper,weproposeadescriptorcombination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This ...
    Sabine Barrat, Salvatore Tabbone
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    JCB
    2006
    185views more  JCB 2006»
    A Probabilistic Methodology for Integrating Knowledge and Experiments on Biological Networks
    13 years 7 months ago
    A Probabilistic Methodology for Integrating Knowledge and Experiments on Biological Networks
    Download  www.math.tau.ac.il
    Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed ...
    Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
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    ICML
    2009
    IEEE
    175views Machine Learning» more  ICML 2009»
    Approximate inference for planning in stochastic relational worlds
    14 years 8 months ago
    Approximate inference for planning in stochastic relational worlds
    Download  user.cs.tu-berlin.de
    Relational world models that can be learned from experience in stochastic domains have received significant attention recently. However, efficient planning using these models rema...
    Tobias Lang, Marc Toussaint
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