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    COOPIS
    2004
    IEEE
    158views Information Technology» more  COOPIS 2004»
    Learning Classifiers from Semantically Heterogeneous Data
    15 years 6 months ago
    Learning Classifiers from Semantically Heterogeneous Data
    Download  www.cs.iastate.edu
    Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
    Doina Caragea, Jyotishman Pathak, Vasant Honavar
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    JWSR
    2006
    168views more  JWSR 2006»
    Facilitating the Specification of Semantic Web Services Using Model-Driven Development
    15 years 2 months ago
    Facilitating the Specification of Semantic Web Services Using Model-Driven Development
    Download  gannodss.csamu.org
    The Semantic Web promises automated invocation, discovery, and composition of Web services by enhancing services with semantic descriptions. An upper ontology for Web services cal...
    Gerald C. Gannod, John T. E. Timm, Raynette J. Bro...
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    123
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    NLE
    2007
    148views more  NLE 2007»
    Abbreviated text input using language modeling
    15 years 2 months ago
    Abbreviated text input using language modeling
    Download  www.eecs.harvard.edu
    We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by ta...
    Stuart M. Shieber, Rani Nelken
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    125
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    DMIN
    2006
    134views Data Mining» more  DMIN 2006»
    Hyper-Rectangular and k-Nearest-Neighbor Models in Stochastic Discrimination
    15 years 4 months ago
    Hyper-Rectangular and k-Nearest-Neighbor Models in Stochastic Discrimination
    Download  ect.bell-labs.com
    The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
    Iryna Skrypnyk, Tin Kam Ho
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    137
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    SDM
    2007
    SIAM
    176views Data Mining» more  SDM 2007»
    Adaptive Concept Learning through Clustering and Aggregation of Relational Data
    15 years 4 months ago
    Adaptive Concept Learning through Clustering and Aggregation of Relational Data
    Download  siam.org
    We introduce a new approach for Clustering and Aggregating Relational Data (CARD). We assume that data is available in a relational form, where we only have information about the ...
    Hichem Frigui, Cheul Hwang
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