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Download Towards a Unified Modeling and Knowledge-Representation by Vassilis G. Kaburlasos PDF

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By Vassilis G. Kaburlasos

By ‘model’ we suggest a mathematical description of an international element. With the proliferation of pcs numerous modeling paradigms emerged less than computational intelligence and tender computing. An advancing expertise is at present fragmented due, besides, to the necessity to take care of types of information in several program domain names. This examine monograph proposes a unified, cross-fertilizing strategy for knowledge-representation and modeling according to lattice theory. The emphasis is on clustering, type, and regression functions. it's proven how rigorous research and layout may be pursued in tender computing utilizing traditional (hard computing) tools. furthermore, non-Turing computation will be pursued. the fabric here's multi-disciplinary according to our on-going learn released in significant clinical journals and meetings. Experimental effects via a variety of algorithms are validated generally. proper paintings by means of different authors can be offered either commonly and comparatively.

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Extra info for Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications

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In the aforementioned context semantic multimedia retrieval has been described as a promising (multimodal) pattern recognition problem (Naphade and Huang 2002). 2 Other Modeling Paradigms 11 for modeling in hypermedia applications, the latter are collections of hypermedia documents organized into a hypertext net. However, a difficulty arises from the need to model effectively general navigational structures, interactive behaviors, and security policies (Díaz et al. 2001). An ontology has been defined as a (non-numeric) formal specification of a shared conceptualization (Chandrasekaran et al.

The core methodologies of CI include neural networks inspired from brain, fuzzy (inference) systems (FIS) inspired from natural language, and evolutionary computing inspired from Darwinian evolution theory. Additional methodologies include probabilistic computing, machine learning, cognitive artificial intelligence, etc. SC methodologies are frequently used for designing devices with high machine intelligence quotient (MHIQ). An interesting connection with conventional artificial intelligence (AI) is shown next.

It follows that U is a complete lattice with least element (0,…,0) and greatest element (1,…,1). Moreover, τ(U) is an atomic lattice because every hyperbox is the lattice-join of atoms ‘min’ and ‘MAX’. A number of computational intelligence schemes that learn by computing hyperboxes have been proposed including min-max neural networks (Simpson 1992, 1993; Gabrys and Bargiela 2000) as well as adaptive resonance theory (ART) inspired neural networks (Georgiopoulos et al. 1994; Healy and Caudell 1997; Cano Izquierdo et al.

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