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Abstract

We consider a multimeasure with the Radon-Nikodym derivative and apply its Castaing representation to get a representation of the multimeasure.

Works Cited Allport, Gordon Willard. 1962. “The General and the Unique in Psychological Science.” Journal of Personality no. 30: 405–422. Barthmaier, Paul. 2014. “What is the Meaning of ʻUnderlying Representation’ in Linguistics? Three Answers.” https://www.quora.com/ (Last accessed 15 March 2016) Benvenuti, Leonardo. 2002. Malattie Mediali. Elementi di socioterapia. [ Mass Media Mental Disorders. Elements of Sociotherapy. ] Bologna: Barkerville. Benvenuti, Leonardo. 2008. Lezioni di Socioterapia. [ Lessons of Sociotherapy. ] Bologna: Baskerville. Dorland

REFERENCES [1] APOSTOL, T. M.: Introduction to Analytic Number Theory . Springer-Verlag, New York-Heidelberg 1976. [2] CILLERUELO, J.—NATHANSON, M. B.: Dense sets of integers with prescribed representation functions , European J. Combin. 34 (2013), no. 8, 1297–1306. [3] DUBICKAS, A.: A basis of finite and infinite sets with small representation function , Electron. J. Combin. 19 (2012), no. 1, Paper 6, 16 pp. [4] –––––– On the supremum of the representation function of a sumset ,Quaest. Math. 37 (2014), no. 1, 1–8. [5] ERDŐS, P.—TURÁN, P.: On a problem

hierarchical and associative systems may imply the possibility for inferencing (e.g., the parent-child class relations in a hierarchical classification or the broader and narrower terms in a thesaurus). By contrast, knowledge representation (KR) in artificial intelligence (AI) applications produces a set of statements that express facts, relations, and conditions in formal languages or schemes upon which reasoning can be performed to determine actions or reach conclusions. The reasoning component is perhaps the most striking difference in KR between traditional knowledge

.R.R. Uijlings, A.W.M. Smeulders, and R.J.H. Scha, Real-time visual concept classification. Multimedia, IEEE Transactions on 12 (2010) 665-681. [5] Y.G. Jiang, J. Yang, C.W. Ngo, and A.G. Hauptmann, Representations of keypoint-based semantic concept detection: A comprehensive study. Multimedia, IEEE Transactions on 12 (2010) 42-53. [6] S. Edelman, Computational theories of object recognition. Trends in Cognitive Sciences 1 (1997) 296-304. [7] B.J. Stankiewicz, and J.E. Hummel, MetriCat: A representation for basic and subordinate-level classification, Lawrence Erlbaum, 1996, pp

University Press, 2000. Řehůřek R., Sojka P. (2010) Software Framework for Topic Modelling with Large Corpora, Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 2010. Reuters Corpus, [Online]. http://about.reuters.com/researchandstandards/corpus/, Released in November 2000. Schoelkopf B., Smola A. (2002) Learning with Kernels, Support Vector Machines, MIT Press, London, 2002. Vintan L., Morariu D., Cretulescu R., Vintan M.. (2017) An Extension of the VSM Documents Representation, International Journal of Computers, Communications & Control, ISSN

Belgium. Journal of Elections, Public Opinion and Parties, 21(1), pp. 97-119. Pitkin, H. F., 1967. The Concept of Representation. University of California Press. Poguntke, T., 1996. Anti‐party Sentiment ‐ Conceptual Thoughts and Empirical Evidence: Explorations into a Minefield. European Journal of Political Research, 29(3), pp. 319-344. Pop-Eleches, G., 2010. Throwing out the Bums: Protest Voting and Unorthodox Parties after Communism. World Politics, 62(2), pp. 221-260. Račkauskas, R. and Navickas, A., 2015. Rytis Račkauskas: Sieksiu, jog mieste atsirastų reali

: http://www.etc.se/inrikes/v-kraver-att-sl-andrar-policy-efter-sdreklamen. Lovenduski, Joni. 2005. State Feminism and Political Representation. Cambridge: Cambridge University Press. Lovenduski, Joni. 2001. ‘Women and Politics: Minority Representation or Critical Mass?’. Parliamentary Affairs 54(4): 743-758. Magnusdottir, Gunnhildur Lily and Annica Kronsell. 2015. ‘The in(visibility) of gender in Scandinavian climate policy-making.’ International Feminist Journal of Politics 17(2): 308-326. Magnusdottir, Gunnhildur Lily and Annica Kronsell. 2016. ‘The Double

advanced solution for providing machine-readable representations for semantic information ( Allemang & Hendler, 2011 ; Ananiadou & McNaught, 2006 ; Maynard, Li, & Peters, 2008 ; McGuinness et al., 2004 ). Ontologies have several advantages. Firstly, serving as a tool of terminology management, ontologies provide a clear representation and communication of complex semantic relationships. Secondly, they support information exchange among biomedical information systems, especially when the biomedical information is growing rapidly ( Alexander, 2006 ; Kumar, Yip, Smith

, PA, 2013 [20] A. Holland, Lecture 2: Rules based systems, 2010 [21] H. Liu, A. Gegov and M. Cocea, Network Based Rule Representation for Knowledge Discovery and Predictive Modelling, in IEEE International Conference on Fuzzy Systems, Istanbul, 2015 [22] H. Liu, A. Gegov and M. Cocea, Rule Based Systems for Big Data: A Machine Learning Approach, 1 ed., vol. 13, Switzerland: Springer, 2016