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    EXACT
    2007
    119views Applied Computing» more  EXACT 2007»
    Learning Models from Temporal-Logic Properties via Explanations
    13 years 10 months ago
    Learning Models from Temporal-Logic Properties via Explanations
    Download  www.aaai.org
    Given a model and a property expressed in temporal logic, a model checker normally produces a counterexample in case the model does not satisfy the property. This counterexample i...
    Miguel A. Carrillo, David A. Rosenblueth
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    ECML
    1998
    Springer
    98views Machine Learning» more  ECML 1998»
    Predicate Invention and Learning from Positive Examples Only
    13 years 11 months ago
    Predicate Invention and Learning from Positive Examples Only
    Download  www.his.se
    Previous bias shift approaches to predicate invention are not applicable to learning from positive examples only, if a complete hypothesis can be found in the given language, as ne...
    Henrik Boström
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    ICGI
    2010
    Springer
    132views Natural Language Processing» more  ICGI 2010»
    Polynomial-Time Identification of Multiple Context-Free Languages from Positive Data and Membership Queries
    13 years 8 months ago
    Polynomial-Time Identification of Multiple Context-Free Languages from Positive Data and Membership Queries
    Download  www-erato.ist.hokudai.ac.jp
    This paper presents an efficient algorithm that identifies a rich subclass of multiple context-free languages in the limit from positive data and membership queries by observing wh...
    Ryo Yoshinaka
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    TCS
    2008
    79views Theoretical Computer Science» more  TCS 2008»
    Learning indexed families of recursive languages from positive data: A survey
    13 years 7 months ago
    Learning indexed families of recursive languages from positive data: A survey
    Download  www2.cs.uregina.ca
    Steffen Lange, Thomas Zeugmann, Sandra Zilles
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    KDD
    2008
    ACM
    137views Data Mining» more  KDD 2008»
    Learning classifiers from only positive and unlabeled data
    14 years 8 months ago
    Learning classifiers from only positive and unlabeled data
    Download  cseweb.ucsd.edu
    The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
    Charles Elkan, Keith Noto
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