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    BMCBI
    2008
    114views more  BMCBI 2008»
    Combining classifiers for improved classification of proteins from sequence or structure
    13 years 10 months ago
    Combining classifiers for improved classification of proteins from sequence or structure
    Download  www.kyb.tuebingen.mpg.de
    Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
    Iain Melvin, Jason Weston, Christina S. Leslie, Wi...
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    ESANN
    2008
    158views Neural Networks» more  ESANN 2008»
    Learning Data Representations with Sparse Coding Neural Gas
    13 years 11 months ago
    Learning Data Representations with Sparse Coding Neural Gas
    Download  www.dice.ucl.ac.be
    Abstract. We consider the problem of learning an unknown (overcomplete) basis from an unknown sparse linear combination. Introducing the "sparse coding neural gas" algori...
    Kai Labusch, Erhardt Barth, Thomas Martinetz
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    NIPS
    2007
    159views Information Technology» more  NIPS 2007»
    Active Preference Learning with Discrete Choice Data
    13 years 11 months ago
    Active Preference Learning with Discrete Choice Data
    Download  books.nips.cc
    We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an...
    Eric Brochu, Nando de Freitas, Abhijeet Ghosh
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    IJCNLP
    2004
    Springer
    148views Natural Language Processing» more  IJCNLP 2004»
    Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
    14 years 3 months ago
    Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationships
    Download  clair.si.umich.edu
    Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this pap...
    Zhu Zhang, Dragomir R. Radev
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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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