By Tsau Young Lin, Abraham Kandel, Yan-Qing Zhang
This overview quantity introduces the radical clever net conception referred to as computational net intelligence (CWI) in response to computational intelligence (CI) and internet expertise (WT). It takes an in-depth examine hybrid internet intelligence (HWI), that's in accordance with synthetic organic and computational intelligence with internet expertise and is used to construct hybrid clever net platforms that serve stressed and instant clients extra successfully. the fundamental rules of CWI and diverse e-applications of CWI and HWI are mentioned. For completeness, six significant CWI ideas - fuzzy internet intelligence, neural internet intelligence, evolutionary net intelligence, granular internet intelligence, tough internet Intelligence and probabilistic net intelligence - are defined. With the large capability for clever e-business functions of CWI and HWI, those recommendations symbolize the way forward for clever net functions.
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A CFS is generated by overlapping the fragments of the activated concept description. A CFS expresses the meaning of a concept by the activation Part I: Fuzzy, Rough, and Probabilistic Web Intelligence 24 values of other concepts in these fragments. 4 Generation of CFSs To generate CFSs, concepts are activated using the RBF networks as follows. 4. Fig. 4 RBF network structure. The degree of relationship between a prototype vector cj(i-th fragment of the concept description) and an input vector x is measured as 9 4 ( d W x 7Cj>> and dist means the distance.
5 Test data. 6 Example of paths. 12 shows the changes of activation values of some words with “personal computer” and “book” as input to the CFS unit. The activation value of the word “magazine” gets higher as the propagation is carried out and is at the peak in the third to fifth iteration. The word “magazine”, which highly relates to “book”, is associated by iterating propagation of activation values in CFS unit. The activation value of the word “information” is also at the peak in the third to fifth iteration.
Semantically A corresponds to the subset of objects the user liked. Recommender Systems Based o n Representations 13 Our goal here is to use this information provide recommendations over the space M = D - E of unexperienced objects. One approach is to provide a collection of justifications or circumstances which indicate that an object in M is suitable for recommendation. If Rj are a collection of justifications for recommending objects and Rj(di) indicates the degree Rj supports the recommendation of di then the overall recommendation of di is R(di) = Ma~j[Rj(di)].