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    ECIR
    2004
    Springer
    150views Information Technology» more  ECIR 2004»
    NNk Networks for Content-Based Image Retrieval
    13 years 10 months ago
    NNk Networks for Content-Based Image Retrieval
    Download  mmis.doc.ic.ac.uk
    Abstract. This paper describes a novel interaction technique to support content-based image search in large image collections. The idea is to represent each image as a vertex in a ...
    Daniel Heesch, Stefan M. Rüger
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    FMOODS
    2006
    81views Formal Methods» more  FMOODS 2006»
    Generating Instance Models from Meta Models
    13 years 10 months ago
    Generating Instance Models from Meta Models
    Download  tfs.cs.tu-berlin.de
    Meta modeling is a wide-spread technique to define visual languages, with the UML being the most prominent one. Despite several advantages of meta modeling such as ease of use, the...
    Karsten Ehrig, Jochen Malte Küster, Gabriele ...
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    PSB
    2001
    75views Applied Computing» more  PSB 2001»
    PIES, A Protein Interaction Extraction System
    13 years 10 months ago
    PIES, A Protein Interaction Extraction System
    Download  helix-web.stanford.edu
    We consider the problem of extracting, manipulating, and managing biological pathways, especially protein-protein interaction pathways. We discuss here the Protein Interaction Ext...
    Limsoon Wong
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    NIPS
    2003
    116views Information Technology» more  NIPS 2003»
    Unsupervised Context Sensitive Language Acquisition from a Large Corpus
    13 years 10 months ago
    Unsupervised Context Sensitive Language Acquisition from a Large Corpus
    Download  kybele.psych.cornell.edu
    We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. Th...
    Zach Solan, David Horn, Eytan Ruppin, Shimon Edelm...
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    UAI
    2003
    154views Artificial Intelligence» more  UAI 2003»
    Learning Continuous Time Bayesian Networks
    13 years 10 months ago
    Learning Continuous Time Bayesian Networks
    Download  robotics.stanford.edu
    Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
    Uri Nodelman, Christian R. Shelton, Daphne Koller
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