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    ESANN
    2008
    137views Neural Networks» more  ESANN 2008»
    Classification of chestnuts with feature selection by noise resilient classifiers
    15 years 8 months ago
    Classification of chestnuts with feature selection by noise resilient classifiers
    Download  www.dice.ucl.ac.be
    In this paper we solve the problem of classifying chestnut plants according to their place of origin. We compare the results obtained by state of the art classifiers, among which,...
    Elena Roglia, Rossella Cancelliere, Rosa Meo
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    FMOODS
    2007
    96views Formal Methods» more  FMOODS 2007»
    On Formal Analysis of OO Languages Using Rewriting Logic: Designing for Performance
    15 years 8 months ago
    On Formal Analysis of OO Languages Using Rewriting Logic: Designing for Performance
    Download  fsl.cs.uiuc.edu
    Rewriting logic provides a powerful, flexible mechanism for language definition and analysis. This flexibility in design can lead to problems during analysis, as different desi...
    Mark Hills, Grigore Rosu
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    SCAI
    2008
    224views Artificial Intelligence» more  SCAI 2008»
    CBR for Advice Giving in a Data-Intensive Environment
    15 years 8 months ago
    CBR for Advice Giving in a Data-Intensive Environment
    Download  www.idi.ntnu.no
    Reusing past experiences by reasoning from past cases poses particular problems when the input to case retrieval comes from large amounts of online data. Volve has developed a syst...
    Agnar Aamodt
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    AAAI
    2006
    104views Intelligent Agents» more  AAAI 2006»
    On Combining Multiple Classifiers Using an Evidential Approach
    15 years 8 months ago
    On Combining Multiple Classifiers Using an Evidential Approach
    Download  www.aaai.org
    Combining multiple classifiers via combining schemes or meta-learners has led to substantial improvements in many classification problems. One of the challenging tasks is to choos...
    Yaxin Bi, Sally I. McClean, Terry J. Anderson
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    AIPS
    2006
    141views Artificial Intelligence» more  AIPS 2006»
    Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
    15 years 8 months ago
    Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
    Download  www.aaai.org
    We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
    Malcolm J. A. Strens
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