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Titlebook: Data Fusion and Perception; Giacomo Riccia,Hans-Joachim Lenz,Rudolf Kruse Book 2001 Springer-Verlag Wien 2001 Artifical Intelligence.Machi

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书目名称Data Fusion and Perception
编辑Giacomo Riccia,Hans-Joachim Lenz,Rudolf Kruse
视频video
丛书名称CISM International Centre for Mechanical Sciences
图书封面Titlebook: Data Fusion and Perception;  Giacomo Riccia,Hans-Joachim Lenz,Rudolf Kruse Book 2001 Springer-Verlag Wien 2001 Artifical Intelligence.Machi
描述This work is a collection of front-end research papers on data fusion and perceptions. Authors are leading European experts of Artificial Intelligence, Mathematical Statistics and/or Machine Learning. Area overlaps with "Intelligent Data Analysis”, which aims to unscramble latent structures in collected data: Statistical Learning, Model Selection, Information Fusion, Soccer Robots, Fuzzy Quantifiers, Emotions and Artifacts.
出版日期Book 2001
关键词Artifical Intelligence; Machine Learning; Statistics; decision making in Operations Research; artificial
版次1
doihttps://doi.org/10.1007/978-3-7091-2580-9
isbn_softcover978-3-211-83683-5
isbn_ebook978-3-7091-2580-9Series ISSN 0254-1971 Series E-ISSN 2309-3706
issn_series 0254-1971
copyrightSpringer-Verlag Wien 2001
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https://doi.org/10.1007/978-1-4757-9712-1f basis functions or the parameters of a kernel function to be used in a regression of the data. The method combines the well-known Bayesian approach with the maximum likelihood method. The Bayesian approach is applied to a set of models with conventional priors that depend on unknown parameters, an
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https://doi.org/10.1007/978-3-031-27837-2g from several sources. Possibility theory is a representation framework that can model various kinds of information items: numbers, intervals, consonant random sets, special kind of probability families, as well as linguistic information, and uncertain formulae in logical settings. The possibilisti
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https://doi.org/10.1007/978-3-031-27837-2an experts who formulate their knowledge in form of fuzzy if-then rules, and databases of sample data. We discuss how to fuse these different types of knowledge by using neuro-fuzzy methods and present some experimental results. We show how neuro-fuzzy approaches can fuse fuzzy rule sets, induce a r
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https://doi.org/10.1007/978-3-031-27837-2ngle expert. Faced to the task of constructing a large model, we may find that each expert might be specialist in some subset of the complete domain. It may be desirable to aggregate the knowledge provided by those specialists, under the form of related graphical models, into a single more general r
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Trichotillomania (Hair-Pulling Disorder)omers extracted from autonomous sites or of an administrative record census. The first example is related to customer relationship management (CRM), while the last one is a substitute of a regular census. This kind of data fusion causes problems of (schema) integration, solving semantic conflicts, a
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Trichotillomania (Hair-Pulling Disorder)on fusion. We restrict the presentation to the problem of information fusion under imprecision and uncertainty, and to numerical methods to account for these imperfections in the fusion process. An illustrative example in brain imaging is sketched.
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