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    GECCO
    2009
    Springer
    204views Optimization» more  GECCO 2009»
    Combined structure and motion extraction from visual data using evolutionary active learning
    14 years 1 days 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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    WAIM
    2010
    Springer
    209views Information Technology» more  WAIM 2010»
    Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
    14 years 14 days ago
    Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
    Download  www.cs.sfu.ca
    Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
    Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...
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    ICCV
    1998
    IEEE
    464views Computer Vision» more  ICCV 1998»
    Automatic Generation of Robot Program Code: Learning from Perceptual Data
    14 years 9 months ago
    Automatic Generation of Robot Program Code: Learning from Perceptual Data
    Download  www.cse.psu.edu
    Mohammed Yeasin, Subhasis Chaudhuri
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    RECOMB
    2004
    Springer
    103views Computational Biology» more  RECOMB 2004»
    Learning multiple evolutionary pathways from cross-sectional data
    14 years 7 months ago
    Learning multiple evolutionary pathways from cross-sectional data
    Download  mtreemix.bioinf.mpi-sb.mpg.de
    Niko Beerenwinkel, Jörg Rahnenführer, Ma...
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    SENSYS
    2009
    ACM
    160views Sensor Networks» more  SENSYS 2009»
    Learning from sensor network data
    14 years 2 months ago
    Learning from sensor network data
    Download  www.tik.ee.ethz.ch
    Matthias Keller, Jan Beutel, Andreas Meier, Roman ...
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