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    AAAI
    2006
    100views Intelligent Agents» more  AAAI 2006»
    Quantifying the Impact of Learning Algorithm Parameter Tuning
    13 years 11 months ago
    Quantifying the Impact of Learning Algorithm Parameter Tuning
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    The impact of learning algorithm optimization by means of parameter tuning is studied. To do this, two quality attributes, sensitivity and classification performance, are investig...
    Niklas Lavesson, Paul Davidsson
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    AAAI
    2006
    166views Intelligent Agents» more  AAAI 2006»
    Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification
    13 years 11 months ago
    Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification
    Download  www.cs.umass.edu
    This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional proba...
    Andrew McCallum, Chris Pal, Gregory Druck, Xuerui ...
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    NIPS
    2004
    110views Information Technology» more  NIPS 2004»
    Sampling Methods for Unsupervised Learning
    13 years 11 months ago
    Sampling Methods for Unsupervised Learning
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    We present an algorithm to overcome the local maxima problem in estimating the parameters of mixture models. It combines existing approaches from both EM and a robust fitting algo...
    Robert Fergus, Andrew Zisserman, Pietro Perona
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    AAAI
    1996
    118views Intelligent Agents» more  AAAI 1996»
    Monitoring Frog Communities: An Application of Machine Learning
    13 years 11 months ago
    Monitoring Frog Communities: An Application of Machine Learning
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    Automatic recognition of animal vocalisations would be a valuable tool for a variety of biological research and environmental monitoring applications . We report the development o...
    Andrew Taylor, Graeme Watson, Gordon Grigg, Hamish...
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    NIPS
    1994
    178views Information Technology» more  NIPS 1994»
    Generalization in Reinforcement Learning: Safely Approximating the Value Function
    13 years 11 months ago
    Generalization in Reinforcement Learning: Safely Approximating the Value Function
    Download  www.ri.cmu.edu
    To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
    Justin A. Boyan, Andrew W. Moore
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