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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2012

01.09.2012 | LSMS2010 and ICSEE2010

Training support vector data descriptors using converging linear particle swarm optimization

verfasst von: Hongbo Wang, Guangzhou Zhao, Nan Li

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

It is known that Support Vector Domain Description (SVDD) has been introduced to detect novel data or outliers. The key problem of training a SVDD is equivalent to solving constrained quadratic programming problem. The Linear Particle Swarm Optimization (LPSO) is developed to optimize linear constrained functions, which is intuitive and simple to implement. However, premature convergence would be followed with the LPSO. The LPSO is extended to the Converging Liner PSO (CLPSO), which is always guaranteed to find at least a local optimum. A new method using CLPSO to train SVDD was proposed. Experimental results demonstrated that the proposed method was feasible and effective for SVDD training, and the performance of it was better than that of traditional method.
Vorheriger Artikel A hybrid genetic algorithm for two-stage multi-item inventory system with stochastic demand
Nächster Artikel Pareto-optimal solutions based multi-objective particle swarm optimization control for batch processes

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Literatur
1.
Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New YorkMATH
2.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
3.
Hsu CW, Lin CJ (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw 2(13):415–425
4.
Zhou HY, Miller P, Zhang JG (2011) Age classification using Radon transform and entropy based scaling SVM. In: Proceedings of British machine vision conference, Dundee, UK, pp 1–12
5.
Chi XJ, Dong JY, Liu AH, et al. (2006) A simple method for Chinese license plate recognition based on support vector machine. In: Proceedings of the 4th international conference on communications, circuits and systems, pp 2141–2145
6.
Collins M, Zhang JG, Miller P, et al. (2010) EigenBody: analysis of body shape for gender from noisy images. In: International machine vision and image processing conference, 2010
7.
David MJT, Robert PWD (2004) Support vector data description. Mach Learn 54(1):45–66MATHCrossRef
8.
Vapnik VN, Kotz S (2006) Estimation of dependences based on empirical data. Springer, New YorkMATH
9.
Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. Published by the IEEE Computer Society
10.
Osuna E, Freund R, Girosi F (1997) An improved training algorithm for support vector machines. Citeseer 276–285
11.
Platt JC (1998) Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in Kernel methods—support vector learning. MIT Press, Boston, pp 185–208
12.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948
13.
Yuan H, Zhang Y, Zhang D, et al. (2006) A modified particle swarm optimization algorithm for support vector machine training. In: The sixth world congress on intelligent control and automation, pp 4128–4132
14.
Paquet U, Engelbrecht AP (2003) A new particle swarm optimiser for linearly constrained optimization. In: The 2003 congress on evolutionary computation, vol 1, pp 227–233
15.
Paquet U, Engelbrecht AP (2003) Training support vector machines with particle swarms. In: Proceedings of the international joint conference on neural networks, vol 2, pp 1593–1598
16.
Paquet U, Engelbrecht AP (2007) Particle swarms for linearly constrained optimisation. Fund Inform 76(1):147–170MathSciNetMATH
17.
van den Bergh F, Engelbrecht AP (2002) A new locally convergent particle swarm optimizer. In: IEEE international conference on systems, man and cybernetics, vol 3, pp 6–11
18.
Angeline P (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. Evol Program VII:601–610
19.
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. Evol Program VII:591–600
20.
Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation, vol 1, pp 84–88
Metadaten
Titel
Training support vector data descriptors using converging linear particle swarm optimization
verfasst von
Hongbo Wang
Guangzhou Zhao
Nan Li
Publikationsdatum
01.09.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0872-y

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