Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2013

01.05.2013 | Original Article

On Lagrangian twin support vector regression

verfasst von: S. Balasundaram, M. Tanveer

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

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, a simple and linearly convergent Lagrangian support vector machine algorithm for the dual of the twin support vector regression (TSVR) is proposed. Though at the outset the algorithm requires inverse of matrices, it has been shown that they would be obtained by performing matrix subtraction of the identity matrix by a scalar multiple of inverse of a positive semi-definite matrix that arises in the original formulation of TSVR. The algorithm can be easily implemented and does not need any optimization packages. To demonstrate its effectiveness, experiments were performed on well-known synthetic and real-world datasets. Similar or better generalization performance of the proposed method in less training time in comparison with the standard and twin support vector regression methods clearly exhibits its suitability and applicability.
Vorheriger Artikel Constructing support vector machine ensemble with segmentation for imbalanced datasets
Nächster Artikel A privacy-preserving algorithm for distributed training of neural network ensembles

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Balasundaram S, Kapil (2010) On Lagrangian support vector regression. Exp Syst Appl 37:8784–8792CrossRef
2.
Balasundaram S, Kapil (2010) Application of Lagrangian twin support vector machines for classification. In: 2nd international conference on machine learning and computing, ICMLC2010, IEEE Press, pp 193–197
3.
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San FranciscoMATH
4.
Brown MPS, Grundy WN, Lin D et al (2000) Knowledge-based analysis of microarray gene expression data using support vector machine. Proc Nat Acad Sci USA 97(1):262–267CrossRef
5.
6.
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel based learning method. Cambridge University Press, CambridgeCrossRef
7.
8.
Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Patt Anal Mach Intell 29(5):905–910CrossRef
9.
Joachims T, Ndellec C, Rouveriol (1998) Text categorization with support vector machines: learning with many relevant features. In: European conference on machine learning no. 10, Chemnitz, Germany, pp 137–142
10.
Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36:7535–7543CrossRef
11.
Li G, Wen C, Guang G-B, Chen Y (2011) Error tolerance based support vector machine for regression. Neurocomputing 74(5):771–782CrossRef
12.
13.
Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Mach Learn Res 1:161–177MathSciNetMATH
14.
Mangasarian OL, Solodov MV (1993) Nonlinear complementarity as unconstrained and constrained minimization. Math Program B 62:277–297MathSciNetMATHCrossRef
15.
Mangasarian OL, Wild EW (2006) Multisurface proximal support vector classification via generalized eigenvalues. IEEE Trans Patt Anal Mach Intell 28(1):69–74CrossRef
16.
Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines. In: NNSP’97: Neural networks for signal processing VII: Proceedings of IEEE signal processing society workshop, Amelia Island, FL, USA, pp 511–520
17.
Muller KR, Smola AJ, Ratsch G, Schölkopf B, Kohlmorgen J (1999) Using support vector machines for time series prediction. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in Kernel methods- support vector learning. MIT Press, Cambridge, pp 243–254
18.
19.
Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of computer vision and pattern recognition, pp 130–136
20.
Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372CrossRef
21.
Ribeiro B (2002) Kernelized based functions with Minkovsky’s norm for SVM regression. In: Proceedings of the international joint conference on neural networks, 2002, IEEE press, pp 2198–2203
22.
Shao Y-H, Zhang C-H, Wang X-B, Deng N-Y (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968CrossRef
23.
24.
Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New YorkMATH
25.
Metadaten
Titel
On Lagrangian twin support vector regression
verfasst von
S. Balasundaram
M. Tanveer
Publikationsdatum
01.05.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0971-9

Weitere Artikel der Sonderheft 1/2013

Neural Computing and Applications 1/2013 Zur Ausgabe

Original Article

Application of feature-weighted Support Vector regression using grey correlation degree to stock price forecasting

Original Article

Specificity enhancement in classification of breast MRI lesion based on multi-classifier

Original Article

Hermite-neural-network-based adaptive control for a coupled nonlinear chaotic system

Original Article

Research of semi-supervised spectral clustering based on constraints expansion

Original Article

Privacy preserving association rule mining over distributed databases using genetic algorithm

Original Article

The dynamic model of lateral inhibition network and it is application

Premium Partner

    Bildnachweise