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Bridging the Gap between Distance and Generalization

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Bridging the Gap between Distance and Generalization

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Estruch Gregori, V.; Ferri Ramírez, C.; José Hernández-Orallo; Ramírez Quintana, MJ. (2012). Bridging the Gap between Distance and Generalization. Computational Intelligence. https://doi.org/10.1111/coin.12004

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/34946

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Title: Bridging the Gap between Distance and Generalization
Author: Estruch Gregori, Vicente Ferri Ramírez, César José Hernández-Orallo Ramírez Quintana, María José
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
Distance-based and generalization-based methods are two families of artificial intelligence techniques that have been successfully used over a wide range of real-world problems. In the first case, general algorithms can ...[+]
Subjects: Learning from structured data representations , Comprehensible models , Distance-based methods , Generalization operators , Minimal generalization
Copyrigths: Cerrado
Source:
Computational Intelligence. (issn: 0824-7935 ) (eissn: 1467-8640 )
DOI: 10.1111/coin.12004
Publisher:
Wiley-Blackwell
Publisher version: http://dx.doi.org/10.1111/coin.12004
Project ID:
info:eu-repo/grantAgreement/MICINN//TIN2010-21062-C02-02/ES/SWEETLOGICS-UPV/
info:eu-repo/grantAgreement/Generalitat Valenciana//PROMETEO08%2F2008%2F051/ES/Advances on Agreement Technologies for Computational Entities (atforce)/
info:eu-repo/grantAgreement/MEC//CSD2007-00022/ES/Agreement Technologies/
Thanks:
We would like to thank the anonymous reviewers for their insightful comments. This work has been partially supported by the EU (FEDER) and the Spanish MICINN, under grant TIN2010-21062-C02-02, the Spanish project "Agreement ...[+]
Type: Artículo

References

Armengol , E. E. Plaza S. Ontanón 2004 Explaining similarity in CBR In ECCBR 2004 Workshop Proceedings 155 164

Bargiela, A., & Pedrycz, W. (2003). Granular Computing. doi:10.1007/978-1-4615-1033-8

Bunke, H. (1997). On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters, 18(8), 689-694. doi:10.1016/s0167-8655(97)00060-3 [+]
Armengol , E. E. Plaza S. Ontanón 2004 Explaining similarity in CBR In ECCBR 2004 Workshop Proceedings 155 164

Bargiela, A., & Pedrycz, W. (2003). Granular Computing. doi:10.1007/978-1-4615-1033-8

Bunke, H. (1997). On a relation between graph edit distance and maximum common subgraph. Pattern Recognition Letters, 18(8), 689-694. doi:10.1016/s0167-8655(97)00060-3

Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/tit.1967.1053964

Develin, M. (2006). Dimensions of Tight Spans. Annals of Combinatorics, 10(1), 53-61. doi:10.1007/s00026-006-0273-y

Domingos, P. (1996). Unifying instance-based and rule-based induction. Machine Learning, 24(2), 141-168. doi:10.1007/bf00058656

Driessens, K., & Džeroski, S. (2005). Combining model-based and instance-based learning for first order regression. Proceedings of the 22nd international conference on Machine learning - ICML ’05. doi:10.1145/1102351.1102376

Eiter, T., & Mannila, H. (1997). Distance measures for point sets and their computation. Acta Informatica, 34(2), 109-133. doi:10.1007/s002360050075

Estruch , V. 2008 Bridging the gap between distance and generalisation: Symbolic learning in metric spaces Ph. D. Thesis http://www.dsic.upv.es/~flip/papers/thesisvestruch.pdf

Estruch , V. C. Ferri J. Hernández-Orallo M. Ramírez-Quintana 2010 Generalisation operators for lists embedded in a metric space In Approaches and Applications of Inductive Programming, Third International Workshop, AAIP 2009 5812 117 139

Estruch , V. C. Ferri J. Hernández-Orallo M. J. Ramírez-Quintana 2005 Distance based generalisation In the 15th International Conference on Inductive Logic Programming, Volume 3625 of LNCS 87 102

Estruch , V. C. Ferri J. Hernández-Orallo M. J. Ramírez-Quintana 2006a Minimal distance-based generalisation operators for first-order objects In the 16th International Conference on Inductive Logic Programming 169 183

Estruch, V., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2006). Web Categorisation Using Distance-Based Decision Trees. Electronic Notes in Theoretical Computer Science, 157(2), 35-40. doi:10.1016/j.entcs.2005.12.043

Finnie, G., & Sun, Z. (2002). Similarity and metrics in case-based reasoning. International Journal of Intelligent Systems, 17(3), 273-287. doi:10.1002/int.10021

Frank , A. A. Asuncion 2010 UCI machine learning repository http://archive.ics.uci.edu/ml

Funes, A., Ferri, C., Hernández-Orallo, J., & Ramírez-Quintana, M. J. (2009). An Instantiation of Hierarchical Distance-Based Conceptual Clustering for Propositional Learning. Lecture Notes in Computer Science, 637-646. doi:10.1007/978-3-642-01307-2_63

Gärtner, T., Lloyd, J. W., & Flach, P. A. (2004). Kernels and Distances for Structured Data. Machine Learning, 57(3), 205-232. doi:10.1023/b:mach.0000039777.23772.30

Gao , B 2006 Hyper-rectangle-based discriminative data generalization and applications in data mining Ph. D. Thesis Simon Frasier University

Golding , A. P. Rosenbloom 1991 Improving rule-based systems through case-based reasoning In National Conference on Artificial Intelligence 22 27

Hahn, U., Chater, N., & Richardson, L. B. (2003). Similarity as transformation. Cognition, 87(1), 1-32. doi:10.1016/s0010-0277(02)00184-1

Hu , C. 2008 Interval rule matrices for decision making In Knowledge Processing with Interval and Soft Computing, Chapter 6 Edited by Springer 135 146

Juszczak, P., Tax, D. M. J., Pe¸kalska, E., & Duin, R. P. W. (2009). Minimum spanning tree based one-class classifier. Neurocomputing, 72(7-9), 1859-1869. doi:10.1016/j.neucom.2008.05.003

Kearfott , R. C. Hu 2008 Fundamentals of interval computing In Knowledge Processing with Interval and Soft Computing, Chapter 1 Edited by Spinger 1 12

Muggleton, S. (1999). Inductive Logic Programming: Issues, results and the challenge of Learning Language in Logic. Artificial Intelligence, 114(1-2), 283-296. doi:10.1016/s0004-3702(99)00067-3

Piramuthu, S., & Sikora, R. T. (2009). Iterative feature construction for improving inductive learning algorithms. Expert Systems with Applications, 36(2), 3401-3406. doi:10.1016/j.eswa.2008.02.010

De Raedt, L., & Ramon, J. (2009). Deriving distance metrics from generality relations. Pattern Recognition Letters, 30(3), 187-191. doi:10.1016/j.patrec.2008.09.007

Ramon , J. M. Bruynooghe 1998 A framework for defining distances between first-order logic objects In Proceedings of the International Conference on Inductive Logic Programming, Volume, 1446 of LNCS 271 280

Rissanen, J. (1999). Hypothesis Selection and Testing by the MDL Principle. The Computer Journal, 42(4), 260-269. doi:10.1093/comjnl/42.4.260

Salzberg, S. (1991). A nearest hyperrectangle learning method. Machine Learning, 6(3), 251-276. doi:10.1007/bf00114779

Stanfill, C., & Waltz, D. (1986). Toward memory-based reasoning. Communications of the ACM, 29(12), 1213-1228. doi:10.1145/7902.7906

Vapnik, V. N., & Chervonenkis, A. Y. (1971). On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. Theory of Probability & Its Applications, 16(2), 264-280. doi:10.1137/1116025

Wallace, C. S. (1999). Minimum Message Length and Kolmogorov Complexity. The Computer Journal, 42(4), 270-283. doi:10.1093/comjnl/42.4.270

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