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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.07.2013 | Original Article

An ε-twin support vector machine for regression

verfasst von: Yuan-Hai Shao, Chun-Hua Zhang, Zhi-Min Yang, Ling Jing, Nai-Yang Deng

Erschienen in: Neural Computing and Applications | Ausgabe 1/2013

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Abstract

This study proposes a new regressor—ε-twin support vector regression (ε-TSVR) based on TSVR. ε-TSVR determines a pair of ε-insensitive proximal functions by solving two related SVM-type problems. Different form only empirical risk minimization is implemented in TSVR, the structural risk minimization principle is implemented by introducing the regularization term in primal problems of our ε-TSVR, yielding the dual problems to be stable positive definite quadratic programming problems, so can improve the performance of regression. In addition, the successive overrelaxation technique is used to solve the optimization problems to speed up the training procedure. Experimental results for both artificial and real datasets show that, compared with the popular ε-SVR, LS-SVR and TSVR, our ε-TSVR has remarkable improvement of generalization performance with short training time.
Vorheriger Artikel Multi-directional two-dimensional PCA with matching score level fusion for face recognition
Nächster Artikel Inverse kinematics of a mobile robot

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Literatur
1.
Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20:273–297MATH
2.
Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATH
3.
Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121–167CrossRef
4.
Deng NY, Tian YJ, Zhang CH (2012) Support vector machines: theory, algorithms, and extensions. CRC Press, Boca Raton
5.
Noble WS (2004) Support vector machine applications in computational biology. In: Schöelkopf B, Tsuda K, Vert J-P (eds) Kernel methods in computational biology. MIT Press, Cambridge, pp 71–92
6.
Lee S, Verri A (2002) Pattern recognition with support vector machines. In: First international workshop, Springer, Niagara Falls, Canada
7.
Ince H, Trafalis TB (2002) Support vector machine for regression and applications to financial forecasting. In: International joint conference on neural networks, Como, Italy, IEEE-INNS-ENNS
8.
Suykens JAK, Lukas L, van Dooren P, De Moor B, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In: Proceedings of European conference of circuit theory design, pp 839–842
9.
Mangasarian OL, Wild EW (2006) Multisurface proximal support vector classification via generalize deigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74CrossRef
10.
Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910CrossRef
11.
Kumar MA, Gopal M (2008) Application of smoothing technique on twin support vector machines. Pattern Recognit Lett 29(13):1842–1848CrossRef
12.
13.
Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535–7543CrossRef
14.
Ghorai S, Mukherjee A, Dutta PK (2009) Nonparallel plane proximal classifier. Signal Process 89(4):510–522MATHCrossRef
15.
Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968CrossRef
16.
Shao YH, Deng NY (2012) A coordinate descent margin based-twin support vector machine for classification. Neural Netw 25:114–121MATHCrossRef
17.
Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10–11):2678–2692MATHCrossRef
18.
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300MathSciNetCrossRef
19.
Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372CrossRef
20.
21.
22.
Peng X (2010) Primal twin support vector regression and its sparse approximation. Neurocomputing 73(16–18):2846–2858CrossRef
23.
Peng X (2012) Efficient twin parametric insensitive support vector regression model. Neurocomputing 79:26–38CrossRef
24.
25.
Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge
26.
Bi J, Bennett KP (2003) A geometric approach to support vector regression. Neurocomputing 55:79–108CrossRef
27.
28.
Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. The John Hopkins University Press, BaltimoreMATH
29.
Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of seventh international conference on knowledge and data discovery, San Francisco, pp 77–86
30.
Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5):1032–1037CrossRef
31.
32.
33.
Weisberg S (1985) Applied linear regression seconded. Wiley, New York
34.
Staudte RG, Sheather SJ (1990) Robust estimationand testing: Wiley series in probability and mathematical statistics. Wiley, New YorkCrossRef
35.
Lee CC, Chung PC, Tsai JR, Chang CI (1999) Robust radial basis function neural networks. IEEE Trans Syst Man Cybern B Cybern 29(6):674–685
36.
Eubank RL (1999) Nonparametric regression and spline smoothing statistics: textbooks and monographs, vol 157, seconded. Marcel Dekker, New York
37.
38.
Jiao L, Bo L, Wang L (2007) Fast sparse approximation for least squares support vector machine. IEEE Trans Neural Netw 18:1–13CrossRef
39.
Wen W, Hao Z, Yang X (2008) A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression. Neurocomputing 71:3096–3103CrossRef
Metadaten
Titel
An ε-twin support vector machine for regression
verfasst von
Yuan-Hai Shao
Chun-Hua Zhang
Zhi-Min Yang
Ling Jing
Nai-Yang Deng
Publikationsdatum
01.07.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0924-3

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