Similarity-Based Heterogeneous Neurons in the Context of General Observational Models

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TypeArticle
ConferenceNeural Network World, November 2002.
Subjectgeneral relational structures; heterogeneous data; similarity functions; heterogeneous neuron models
AbstractThis paper presents a framework for processing heterogeneous information based on the construction of general observational domains, and similarity-based function calculi suitable for data mining in domains which can be described by the corresponding observational models. These calculi are intuitive, simple, and sufficiently general for classification and pattern recognition tasks. Functions in these calculi are represented by a particular kind of neuron models and their behavior is illustrated with examples from real-world domains showing their capabilities in processing heterogeneous, incomplete and fuzzy information.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number45822
NPARC number5763862
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Record identifier423d2851-694a-46b6-884b-24087efe21b2
Record created2009-03-29
Record modified2016-05-09
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