Machine Learning Techniques for Understanding Context and Process View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011-08-09

AUTHORS

Marko Grobelnik , Dunja Mladenić , Gregor Leban , Tadej Štajner

ABSTRACT

This chapter discusses how machine learning techniques can be useful for modelling and understanding context and processes. Machine learning techniques that have been applied for understanding context and processes are briefly presented together with the setting in which they have been applied. An example application focusing on context understanding is described to illustrate results of applying the techniques on real-world data. Interpretation and understanding of context in the ACTIVE knowledge workspace is described in 10.1007/978-3-642-19510-5_5 and deployed at BT as described in 10.1007/978-3-642-19510-5_9, while optimizing and sharing of knowledge processes is addressed in 10.1007/978-3-642-19510-5_6. More... »

PAGES

127-145

References to SciGraph publications

  • 2006. Feature Selection for Dimensionality Reduction in SUBSPACE, LATENT STRUCTURE AND FEATURE SELECTION
  • 2010. A Cluster-Level Semi-supervision Model for Interactive Clustering in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2009-04. Mining probabilistic automata: a statistical view of sequential pattern mining in MACHINE LEARNING
  • Book

    TITLE

    Context and Semantics for Knowledge Management

    ISBN

    978-3-642-19509-9
    978-3-642-19510-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-19510-5_7

    DOI

    http://dx.doi.org/10.1007/978-3-642-19510-5_7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1040505565


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