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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書(shū)目名稱(chēng)Data Fusion and Perception影響因子(影響力)




書(shū)目名稱(chēng)Data Fusion and Perception影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Data Fusion and Perception網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Data Fusion and Perception網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Data Fusion and Perception被引頻次




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書(shū)目名稱(chēng)Data Fusion and Perception年度引用




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書(shū)目名稱(chēng)Data Fusion and Perception讀者反饋




書(shū)目名稱(chēng)Data Fusion and Perception讀者反饋學(xué)科排名

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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

亞當(dāng)心理陰影

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

土產(chǎn)

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

期滿(mǎn)

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

期滿(mǎn)

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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embolus

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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