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    TREC
    2001
    132views Information Technology» more  TREC 2001»
    IIT at TREC-10
    14 years 11 months ago
    IIT at TREC-10
    Download  trec.nist.gov
    For TREC-10, we participated in the adhoc and manual web tracks and in both the site-finding and cross-lingual tracks. For the adhoc track, we did extensive calibrations and learn...
    Mohammed Aljlayl, Steven M. Beitzel, Eric C. Jense...
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    CEC
    2005
    IEEE
    123views Artificial Intelligence» more  CEC 2005»
    Population based incremental learning with guided mutation versus genetic algorithms: iterated prisoners dilemma
    15 years 1 days ago
    Population based incremental learning with guided mutation versus genetic algorithms: iterated prisoners dilemma
    Download  cswww.essex.ac.uk
    Axelrod’s original experiments for evolving IPD player strategies involved the use of a basic GA. In this paper we examine how well a simple GA performs against the more recent P...
    Timothy Gosling, Nanlin Jin, Edward P. K. Tsang
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    ECAI
    2008
    Springer
    95views Artificial Intelligence» more  ECAI 2008»
    Determining Automatically the Size of Learned Ontologies
    14 years 12 months ago
    Determining Automatically the Size of Learned Ontologies
    Download  users.iit.demokritos.gr
    Determining the size of an ontology that is automatically learned from texts is an open issue. In this paper, we study the similarity between ontology concepts at different levels ...
    Elias Zavitsanos, Sergios Petridis, Georgios Palio...
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    ACL
    2008
    96views Computational Linguistics» more  ACL 2008»
    Using Automatically Transcribed Dialogs to Learn User Models in a Spoken Dialog System
    14 years 11 months ago
    Using Automatically Transcribed Dialogs to Learn User Models in a Spoken Dialog System
    Download  www.aclweb.org
    We use an EM algorithm to learn user models in a spoken dialog system. Our method requires automatically transcribed (with ASR) dialog corpora, plus a model of transcription error...
    Umar Syed, Jason Williams
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    NIPS
    2007
    155views Information Technology» more  NIPS 2007»
    Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
    14 years 11 months ago
    Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
    Download  books.nips.cc
    We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...
    Ruslan Salakhutdinov, Geoffrey E. Hinton
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