Expansible and Reductible Computable Aggregation Rules
| dc.book.title | Actas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial | |
| dc.contributor.author | González Del Campo Rodríguez Barbero, Ramón | |
| dc.contributor.author | Garmendia Salvador, Luis | |
| dc.contributor.author | Montero De Juan, Francisco Javier | |
| dc.date.accessioned | 2023-06-19T15:55:06Z | |
| dc.date.available | 2023-06-19T15:55:06Z | |
| dc.date.issued | 2015 | |
| dc.description | V Simposio de Lógica Difusa y Soft Computing. | |
| dc.description.abstract | The aggregation operator have been considered from a computable point of view. The important condition that the computation is friendly when portions of data are inserted o deleted to the list of values to aggregate is considered. | en |
| dc.description.department | Depto. de Estadística e Investigación Operativa | |
| dc.description.faculty | Fac. de Ciencias Matemáticas | |
| dc.description.refereed | TRUE | |
| dc.description.sponsorship | Comunidad de Madrid | |
| dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades (España) | |
| dc.description.status | pub | |
| dc.eprint.id | https://eprints.ucm.es/id/eprint/34821 | |
| dc.identifier.isbn | 978-84-608-4099-2 | |
| dc.identifier.officialurl | http://simd.albacete.org/actascaepia15/papers/01261.pdf | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14352/35794 | |
| dc.language.iso | eng | |
| dc.page.final | 517 | |
| dc.page.initial | 511 | |
| dc.page.total | 1265 | |
| dc.publication.place | Albacete | |
| dc.publisher | CAEPIA'15 | |
| dc.relation.projectID | CASI-CM (S2013/ICE-2845) | |
| dc.relation.projectID | C080020-09 | |
| dc.relation.projectID | TIN2013-41592-P | |
| dc.relation.projectID | LODISCO TIN2014-56381-REDT | |
| dc.rights.accessRights | restricted access | |
| dc.subject.cdu | 004.8 | |
| dc.subject.ucm | Inteligencia artificial (Informática) | |
| dc.subject.unesco | 1203.04 Inteligencia Artificial | |
| dc.title | Expansible and Reductible Computable Aggregation Rules | en |
| dc.type | book part | |
| dcterms.references | 1. Concha Bielza and Pedro Larrañaga. Discrete Bayesian network classifier: A survey. ACM Computing Surveys, 47(1), 2014. Article 5. 2. Hanen Borchani, Gherardo Varando, Concha Bielza, and Pedro Larra˜naga. A survey on multi-output regression. WIREs Data Mining and Knowledge Discovery,2015, (in press). 3. Pedro Domingos and Michael Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2-3):103–130, 1997. 4. Nir Friedman, Dan Geiger, and Moises Goldszmidt. Bayesian network classifiers. Machine Learning, 29(2-3):131–163,1997. 5. Manfred Jaeger. Probabilistic classifiers and the oncepts they recognize. In Tom Fawcett and Nina Mishra, editors, Proceedings of the Twentieth International Conference on Machine Learning (ICML-03), pages 266–273. AAAI Press,2003. 6. Yushi Jing, Vladimir Pavlovic, and James M. Rehg. Efficient discriminative learning of bayesian network classifier via boosted augmented naive bayes. In Proceedings of the 22Nd International Conference on Machine Learning, CML’05, pages 369–376. ACM, 2005. 7. Charles X. Ling and Huajie Zhang. The representational power of discrete Bayesian networks. Journal of Machine Learning Research, 3:709–721, 2003. 8. Marvin Minsky. Steps toward artificial intelligence. In Computers and Thought,pages 406–450. McGraw-Hill, 1961. 9. T. Mouratis and S. Kotsiantis. Increasing the accuracy of discriminative of multinomial bayesian classifier in text classification. In Computer Sciences and Convergence Information Technology, 2009. ICCIT ’09. Fourth nternational Conference on, pages 1246–1251, 2009. 10. Mark A. Peot. Geometric implications of the naive Bayes assumption. In Horvitz Eric and Jensen Finn, editors, roceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence, pages 414–419. organ Kaufmann Publishers Inc., 1996. 11. G. Varando, P.L. Lopez-Cruz, T. Nielsen, P. Larrañaga,and C. Bielza. Conditional density approximations with mixtures of polynomials. International Journal of Intelligent Systems, 30(3):236–264, 2015. 12. Gherardo Varando, Concha Bielza, and Pedro Larrañaga. Expressive power of binary relevance and chain classifiers based on Bayesian networks for multi-label classification. In Linda C. van der Gaag and Ad J. Feelders, editors, robabilistic Graphical Models, volume 8754 of Lecture Notes in Computer Science, pages 519–534. Springer, 2014. 13. Gherardo Varando, Concha Bielza, and Pedro Larrañaga. Bnetapp an rbnet graphical interface, 2015. 14. Gherardo Varando, Concha Bielza, and Pedro Larrañaga. Rbmop: B-spline density estimations, 2015. (beta) ithub.com/gherardovarando/Rbmop. 15. Gherardo Varando, Concha Bielza, and Pedro Larrañaga. Rbnet: Bayesian networks with B-spline, 2015. (beta) ithub.com/gherardovarando/Rbnet. 16. Gherardo Varando, Concha Bielza, and Pedro Larra˜naga. Decision boundary for discrete Bayesian network classifiers. Journal of Machine Learning Research, 2015,(in press). 17. Gherardo Varando, Concha Bielza, and Pedro Larrañaga. Decision functions for chain classifiers based on Bayesian networks for multi-label classification. International Journal of Approximate Reasoning, 2015, (in press). 18. Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman, and Geoffrey I. Webb. Alleviating naive Bayes attribute ndependence assumption by attribute weighting.Journal of Machine Learning Research, 14:1947–1988, 2013. | |
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