Aviso: para depositar documentos, por favor, inicia sesión e identifícate con tu cuenta de correo institucional de la UCM con el botón MI CUENTA UCM. No emplees la opción AUTENTICACIÓN CON CONTRASEÑA
 

Learning stable weights for data of varying dimension

Simple item page
dc.book.titleProceedings of 8th International Summer School on Aggregation Operators
dc.contributor.authorBeliakov, G.
dc.contributor.authorJames, S.
dc.contributor.authorGómez González, Daniel
dc.contributor.authorRodríguez González, Juan Tinguaro
dc.contributor.authorMontero De Juan, Francisco Javier
dc.contributor.editorBaczy�nski, Michal
dc.contributor.editorDe Baets, Bernard
dc.contributor.editorMesiar, Radko
dc.date.accessioned2023-06-19T15:54:48Z
dc.date.available2023-06-19T15:54:48Z
dc.date.issued2015
dc.description.abstractIn this paper we develop a data-driven weight learning method for weighted quasiarithmetic means where the observed data may vary in dimension.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipGobierno de España
dc.description.sponsorshipComunidad de Madrid
dc.description.sponsorshipUniversidad Complutense de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/32436
dc.identifier.isbn978-83-8012-519-3
dc.identifier.officialurlhttp://agop.math.us.edu.pl/AGOP2015_proceedings.pdf#page=49
dc.identifier.urihttps://hdl.handle.net/20.500.14352/35762
dc.language.isoeng
dc.page.final54
dc.page.initial49
dc.page.total244
dc.publication.placePoland
dc.publisherTOTEM.COM.PL SP
dc.relation.projectIDTIN2012-32482
dc.relation.projectIDCASI-CAM-CM (S2013/ICE-2845)
dc.relation.projectIDResearch group 910149
dc.rights.accessRightsopen access
dc.subject.cdu510.6
dc.subject.keywordAggregation functions
dc.subject.keywordRstability
dc.subject.keywordLinear programming
dc.subject.keywordWeights learning
dc.subject.ucmLógica simbólica y matemática (Matemáticas)
dc.subject.unesco1102.14 Lógica Simbólica
dc.titleLearning stable weights for data of varying dimensionen
dc.typebook part
dcterms.references[1]J. Aczel. On mean values. Bulletin of the American Math.Society, 54, 1948. [2] A. Amo, J. Montero, and E. Molina. Representation of consistent recursive rules. European Journal of Operational Research, 130:29–53, 2001. [3] G. Beliakov. Construction of aggregation functions from data using linear programming. Fuzzy Sets and Systems, 160:65–75, 2009. [4] G. Beliakov and S. James. Stability of weighted penalty-based aggregation functions. Fuzzy Sets and Systems, 226:1–18, 2013. [5] G. Beliakov, A. Pradera, and T. Calvo. Aggregation Functions: A Guide for Practitioners. Springer, Heidelberg, 2007. [6] P. Bloomfield and W. Steiger. Least Absolute Deviations. Theory, Applications and Algorithms. Birkhauser, Boston, Basel, Stuttgart, 1983. [7] H. Bustince, B. de Baets, J. Fern´andez, R. Mesiar,and J. Montero. A generalization of the migrativity property of aggregation functions. Information Sciences, 191:76–85,2012. [8] T. Calvo, G. Mayor, J. Torrens, J. Suñer, M. Mas,and M. Carbonell. Generation of weighting triangles associated with aggregation functions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 8(4):417–451, 2000. [9] V. Cutello and J. Montero. Recursive connective rules. Int. J. Intelligent Systems, 14:3–20, 1999. [10] J. Dujmovic. Aggregation operators and observable properties of human reasoning. [11] J. Dujmovic, G. D. Tre, N. Singh, D. Tomasevich,and R. Yokoohji. Soft computing models in online real estate. In M. Jamshidi, V. Kreinovich,and J. Kacprzyk, editors, Advance Trends in Soft Computing, WCSC 2013, Studies in Fuzziness and Soft Computing 312, pages 77–91. Springer,2013. [12] J. J. Dujmovic. The problem of missing data in LSP aggregation. In S. G. et al., editor, Advances in Computational Intelligence, IPMU 2012, Part III, CCIS 299, pages 336 – 346. Springer, Catania,Italy, 2012. [13] D. Gomez and J. Montero. A discussion on aggregation operators. Kybernetika, 40:107–120, 2004. [14] D. Gomez, K. Rojas, J. Montero, J. Rodrıguez,and G. Beliakov. Consistency and stability in aggregation operators, an application to missing data problems. Int. J. Computational Intelligence Systems, 7:595–604, 2014. [15] M. Grabisch, J.-L. Marichal, R. Mesiar, and E. Pap. Aggregation Functions. Cambridge University Press,Cambridge, 2009. [16] D. Nettleton and V. Torra. A comparison of active set method and genetic algorithm approaches for learning weighting vectors in some aggregation operators.International Journal of Intelligent Systems, 16(9):1069–1083, 2001. [17] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria,2011. http://www.R-project.org/. [18] K. Rojas, D. Gomez, J. Montero, and J. T.Rodrıguez. Strictly stable families of aggregation operators. Fuzzy Sets and Systems, 228:44 – 63,2013. [19] K. Rojas, D. Gomez, J. Rodriguez, and J. Montero. Some properties of consistency in the families of aggregation functions. Advances in Intelligent and Soft Computing,107:169–176, 2011. [20] V. Torra and Y. Narukawa. The h-index and the number of citations: Two fuzzy integrals. IEEE Transactions on Fuzzy Systems, 16(3):795–797,2008. [21] Y. Torra and V. Narukawa. Modeling Decisions.Information Fusion and Aggregation Operators.Springer, 2007. [22] R. R. Yager and A. Rybalov. Noncommutative self-identity aggregation. Fuzzy Sets and Systems,85:73–82, 1997.
dspace.entity.typePublication
relation.isAuthorOfPublication4dcf8c54-8545-4232-8acf-c163330fd0fe
relation.isAuthorOfPublicationddad170a-793c-4bdc-b983-98d313c81b03
relation.isAuthorOfPublication9e4cf7df-686c-452d-a98e-7b2602e9e0ea
relation.isAuthorOfPublication.latestForDiscoveryddad170a-793c-4bdc-b983-98d313c81b03

Download

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Montero258.pdf
Size:
978.06 KB
Format:
Adobe Portable Document Format
downoload Download

Collections

Secciones de libros