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    ACL
    1997
    141views Computational Linguistics» more  ACL 1997»
    A Structured Language Model
    13 years 11 months ago
    A Structured Language Model
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    A new language model for speech recognition inspired by linguistic analysis is presented. The model develops hidden hierarchical structure incrementally and uses it to extract mea...
    Ciprian Chelba
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    ISMB
    1997
    154views Computational Biology» more  ISMB 1997»
    Hierarchical Protein Structure Superposition Using Both Secondary Structure and Atomic Representations
    13 years 11 months ago
    Hierarchical Protein Structure Superposition Using Both Secondary Structure and Atomic Representations
    Download  www.aaai.org
    The structural comparison of proteins has become increasingly important as a means to identify protein motifs and fold families. In this paper we present a new algorithm for the c...
    Amit Pal Singh, Douglas L. Brutlag
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    ISIPTA
    2003
    IEEE
    119views Mathematics» more  ISIPTA 2003»
    Analysis of Local or Asymmetric Dependencies in Contingency Tables using the Imprecise Dirichlet Model
    14 years 3 months ago
    Analysis of Local or Asymmetric Dependencies in Contingency Tables using the Imprecise Dirichlet Model
    Download  www.carleton-scientific.com
    We consider the statistical problem of analyzing the association between two categorical variables from cross-classified data. The focus is put on measures which enable one to st...
    Jean-Marc Bernard
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    ICML
    2002
    IEEE
    146views Machine Learning» more  ICML 2002»
    Hierarchically Optimal Average Reward Reinforcement Learning
    14 years 11 months ago
    Hierarchically Optimal Average Reward Reinforcement Learning
    Download  www.cs.ualberta.ca
    Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
    Mohammad Ghavamzadeh, Sridhar Mahadevan
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    IJCAI
    2003
    87views Artificial Intelligence» more  IJCAI 2003»
    A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
    13 years 11 months ago
    A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
    Download  www.ri.cmu.edu
    This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground trut...
    Sebastian Thrun, Geoffrey J. Gordon, Frank Pfennin...
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