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Del Favero, Simone and Varagnolo, Damiano and Dinuzzo, Francesco and Schenato, Luca and Pillonetto, Gianluigi (2011) On the discardability of data in Support Vector Classification problems. [Conference papers] In: IEEE Conference on Decision and Control, 12 - 15 December, 2011, Orlando, Florida, USA.

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Abstract (english)

We analyze the problem of data sets reduction for support vector classification. The work is also motivated by distributed problems, where sensors collect binary measurements at different locations moving inside an environment that needs to be divided into a collection of regions labeled in two different ways. The scope is to let each agent retain and exchange only those measurements that are mostly informative for the collective reconstruction of the decision boundary. For the case of separable classes, we provide the exact conditions and an efficient algorithm to determine if an element in the training set can become a support vector when new data arrive. The analysis is then extended to the non-separable case deriving a sufficient discardability condition and a general data selection scheme for classification. Numerical experiments relative to the distributed problem show that the proposed procedure allows the agents to exchange a small amount of the collected data to obtain a highly predictive decision boundary.

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EPrint type:Conference papers (Relazione)
Anno di Pubblicazione:12 December 2011
Key Words:distributed classification, support vector machines, model reduction, distributed machine learning, simplex, convex analysis
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/04 Automatica
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:4302
Depositato il:29 Sep 2011 15:18
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I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, ``A survey on sensor networks,'' IEEE Communications Magazine, vol. 40, no. 8, pp. 102 -- 114, August 2002. Cerca con Google

D. Puccinelli and M. Haenggi, ``Wireless sensor networks: applications and challenges of ubiquitous sensing,'' IEEE Circuits and Systems Magazine, vol. 5, no. 3, pp. 19 -- 31, 2005. Cerca con Google

J. B. Predd, S. R. Kulkarni, and H. V. Poor, ``Distributed learning in wireless sensor networks,'' IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 56 -- 69, July 2006. Cerca con Google

J.-J. Xiao, A. Ribeiro, Z.-Q. Luo, and G. Giannakis, ``Distributed compression-estimation using wireless sensor networks,'' IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 27 -- 41, July 2006. Cerca con Google

V. N. Vapnik, Statistical Learning Theory. New York: Springer-Verlag, 1998. Cerca con Google

P. A. Forero, A. Cano, and G. B. Giannakis, ``Consensus-based distributed support vector machines,'' Journal of Machine Learning Research, vol. 11, pp. 1663 -- 1707, 2010. Cerca con Google

R. U. Pedersen, ``Using support vector machines for distributed machine learning,'' Ph.D. dissertation, University of Copenhagen, August 2004. Cerca con Google

T. Alpcan and C. Bauckhage, ``A distributed machine learning framework,'' in Proceedings of the 48th IEEE Conference on Decision and Control, December 2009, pp. 2546 -- 2551. Cerca con Google

F. Orabona, J. Keshet, and B. Caputo, ``Bounded kernel-based online learning,'' Journal of Machine Learning Research, vol. 10, pp. 2643--2666, 2009. Cerca con Google

V. N. Vapnik, Estimation of Dependence Based on Empirical Data. Berlin: Springer-Verlag, 1982. Cerca con Google

G. Cauwenberghs and T. Poggio, Incremental and Decremental Support Vector Machine Learning. MIT Press, 2000, vol. 13, pp. 409 -- 415. Cerca con Google

Y. Li, W. Zhang, and C. Lin, ``Simplify support vector machines by iterative learning,'' Neural Information Processing - Letters and Reviews, vol. 10, no. 1, pp. 11 -- 17, January 2006. Cerca con Google

C. Li, K. Liu, and H. Wang, ``The incremental learning algorithm with support vector machine based on hyperplane-distance,'' Applied Intelligence, vol. 34, no. 1, pp. 1 -- 9, 2009. Cerca con Google

T. Downs, K. E. Gates, and A. Masters, ``Exact simplification of support vector solutions,'' Journal of Machine Learning Research, vol. 2, pp. 293 -- 297, December 2001. Cerca con Google

Y. Engel, S. Mannor, and R. Meir, ``Sparse online greedy support vector regression,'' in Machine Learning: ECML 2002. Springer Berlin / Heidelberg, 2002, vol. 2430, pp. 1--3. Cerca con Google

T. Kobayashi and N. Otsu, ``Efficient reduction of support vectors in kernel-based methods,'' in Proceedings of the 16th IEEE International Conference on Image Processing, November 2009, pp. 2077 -- 2080. Cerca con Google

Y.-G. Liu, Q. Chen, and R.-Z. Yu, ``Extract candidates of support vector from training set,'' in Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 5, November 2003, pp. 3199 -- 3202. Cerca con Google

C. J. C. Burges, ``Simplified support vector decision rules,'' in Proceedings of the 13th International Conference on Machine Learning, 1996, pp. 71 -- 77. Cerca con Google

C. J. C. Burges and B. Sch\"olkopf, ``Improving the accuracy and speed of support vector learning machines,'' in Proceedings of the 9th NIPS Conference, 1997, pp. 375 -- 381. Cerca con Google

R. Koggalage and S. Halgamuge, ``Reducing the number of training samples for fast support vector machine classification,'' Neural Information Processing - Letters and Reviews, vol. 2, no. 3, pp. 57 -- 65, March 2004. Cerca con Google

H. Shin and S. Cho, ``Fast pattern selection for support vector classifiers,'' in Advances in Knowledge Discovery and Data Mining, ser. Lecture Notes in Computer Science. Springer Berlin / Heidelberg, 2003, vol. 2637. Cerca con Google

X. Liang, ``An effective method of pruning support vector machine classifiers,'' IEEE Transactions on Neural Networks, vol. 21, no. 1, pp. 26 -- 38, January 2010. Cerca con Google

S. Katagiri and S. Abe, ``Incremental training of support vector machines using hyperspheres,'' Pattern Recognition Letters, vol. 27, no. 13, pp. 1495 -- 1507, October 2006. Cerca con Google

S. Katagiri and S. Abe, ``Incremental training of support vector machines using truncated hypercones,'' in Artificial Neural Networks in Pattern Recognition, ser. Lecture Notes in Computer Science. Springer Berlin / Heidelberg, 2006, vol. 4087, pp. 153 -- 164. Cerca con Google

S. Abe and T. Inoue, ``Fast training of support vector machines by extracting boundary data,'' in Proceedings of the International Conference on Artificial Neural Networks, vol. 2130, 2001, pp. 308 -- 313. Cerca con Google

A. Bordes and L. Bottou, ``The huller: A simple and efficient online svm,'' in Machine Learning: ECML 2005. Springer Berlin / Heidelberg, 2005. Cerca con Google

B. C. Barber, D. P. Dobkin, and H. Huhdanpaa, ``The quickhull algorithm for convex hulls,'' ACM Transactions on Mathematical Software, vol. 22, no. 4, pp. 469 -- 483, December 1996. Cerca con Google

R. Schaback and H. Wendland, ``Kernel techniques: From machine learning to meshless methods,'' Acta Numerica, vol. 15, pp. 543 -- 639, 2006. Cerca con Google

F. Dinuzzo and G. De Nicolao, ``An algebraic characterization of the optimum of regularized kernel methods,'' Machine Learning, vol. 74, no. 3, pp. 315 -- 345, 2009. Cerca con Google

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