Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2013

01-02-2013 | Original Article

Twin support vector regression for the simultaneous learning of a function and its derivatives

Authors: Reshma Khemchandani, Anuj Karpatne, Suresh Chandra

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2013

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Twin support vector regression (TSVR) determines a pair of \(\epsilon\)-insensitive up- and down-bound functions by solving two related support vector machine-type problems, each of which is smaller than that in a classical SVR. On the lines of TSVR, we have proposed a novel regressor for the simultaneous learning of a function and its derivatives, termed as TSVR of a Function and its Derivatives. Results over several functions of more than one variable demonstrate its effectiveness over other existing approaches in terms of improving the estimation accuracy and reducing run time complexity.
previous article Efficient sub-window search with fixed shape sub-windows
next article Capacitated two-stage facility location problem with fuzzy costs and demands

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
Antonio J, Martìn H, Santos M, Lope J (2010) Orthogonal variant moments features in image analysis. Inform Sci 180:846–860MathSciNetCrossRef
2.
Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRef
3.
Beauchemin SS, Barron JL (1995) The computation of optical flow. ACM, New York
4.
5.
Christianini N, Shawe-Taylor J (2000) An intorduction to support vector machines. Cambridge University Press, Cambridge
6.
Ebrahimi T, Garcia G, Vesin J (2002) Joint time-frequency-space classification of EEG in a brain–computer interface appplication. J Apply Signal Process 1:713–729
7.
Fung G, Mangasarian OL (2001) Incremental support vector machine classification. In: 7th ACM SIGKDD international conference on knowledge discovery and data mining, pp 77–86
8.
Golub GH, Van Loan CF (1996) Matrix computations, 3rd edn. The John Hopkins Univ. Press, Maryland
9.
He F, IAENG M, Yeung LM, Brown M (2008) Discrete-Time Model Representation for Biochemical Pathway Systems. IAENG Int J Comput Sci 34(1):1–15
10.
Ince H, Trafalis TB (2000) Support vector machine for regression and applications to financial forecasting, In: International joint conference on neural networks, IJCNN, vol 6, pp 348–353
11.
Jayadeva, Khemchandani R, Chandra S (2006) Regularized least squares twin SVR for the simultaneous learning of a function and its derivative, IJCNN, pp 1192–1197
12.
Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29:905–910
13.
Jayadeva, Khemchandani R, Chandra S (2008) Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives. Inform Sci 178:3402–3414
14.
Joachims T (1999) Making large-scale SVM learning practical. In: Advances in kernel methods: support vector learning. MIT Press, Cambridge
15.
Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. In: Proceedings of the IEEE, pp 401–422
16.
Khemchandani R, Jayadeva, Chandra S (2009) Regularized least squares fuzzy support vector regression for financial time series forecasting. Exp Syst Appl 36:132–138
17.
Lagaris IE, Likas A, Fotiadis D (1998) Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans Neural Netw 9:987–1000CrossRef
18.
Lengagne R (2000) 3D stereo reconstruction of human faces driven by differential constraints. Image Vis Comput 18:337-343CrossRef
19.
Lázaro M, Santamaŕia I, Pérez-Cruz F, Artés-Rodŕiguez A (2005) Support vector regression for the simultaneous learning of a multivariate function and its derivative. Neurocomputing 69:42–61CrossRef
20.
Lázaro M, Santamaria I, Pérez-Cruz F, Artés-Rodriguez A (2003) SVM for the simultaneous approximation of a function and its derivative. In: Proceedings of the 2003 IEEE international workshop on neural networks for signal processing (NNSP), Toulouse, France, pp 189–198
21.
Mangasarian OL (1998) Nonlinear programming. SIAM
22.
Liu Z, Wu Q, Zhang Y, Chen CLP (2011) Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int J Mach Learn Cybern 2(1):37–47MathSciNetCrossRef
23.
Mees AI, Jackson MF, Chua LO (1992) Device modeling by radial basis function. IEEE Trans Circuits Syst I Fundam Theory Appl 39:19–27CrossRef
24.
Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: IEEE computer society conference on computer vision and pattern recognition, pp 130–136
25.
Peng X (2009) TSVR: an efficient twin support vector machine for regression. Neural Netw 23:365–372CrossRef
26.
Pérez-Cruz F, Bousono-Calzón C, Artés-Rodriguez A (2005) Convergence of the IRWLS procedure to the support vector machine solution. Neural Comput 17:7–18MATHCrossRef
27.
Schmidt M, Lipson H (2009) Distilling free-form natural laws from experimental data. Science 324(5923):81–85CrossRef
28.
Kemelmacher-Shlizerman I, Basri R (2011) 3D face reconstruction from a single image using a single reference face shape. IEEE Trans Pattern Anal Mach Intell 33(2):394–405CrossRef
29.
Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin
30.
Vapnik VN (1998) Statistical learning theory. Wiley, NY
31.
Wang Z, Lu S, Zhai J (2008) Fast fuzzy multi-category SVM based on support vector domain description. Int J Pattern Recogn Artif Intell 22(1):109–120CrossRef
32.
33.
Zheng S (2011) Gradient descent algorithms for quantile regression with smooth approximation. Int J Mach Learn Cybern 2(3):191–207CrossRef
Metadata
Title
Twin support vector regression for the simultaneous learning of a function and its derivatives
Authors
Reshma Khemchandani
Anuj Karpatne
Suresh Chandra
Publication date
01-02-2013
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2013
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0072-1

Other articles of this Issue 1/2013

International Journal of Machine Learning and Cybernetics 1/2013 Go to the issue

Original Article

Comparative study on classification performance between support vector machine and logistic regression

Original Article

Capacitated two-stage facility location problem with fuzzy costs and demands

Original Article

Additive and multiplicative mixed normal distributions and finding cluster centers

Original Article

Enhancing gene expression clustering analysis using tangent transformation

Original Article

Efficient sub-window search with fixed shape sub-windows

Original Article

Optimal trajectory planning for the herding problem: a continuous time model