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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2018

16.05.2017

Robust Parametric Twin Support Vector Machine for Pattern Classification

verfasst von: Reshma Rastogi, Sweta Sharma, Suresh Chandra

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

Einloggen

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

search-config
loading …

Abstract

In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric-\(\nu \)-Support Vector Machine (Par-\(\nu \)-SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par-\(\nu \)-SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.
Vorheriger Artikel Robust Video Face Recognition Under Pose Variation

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

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

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"

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
Literatur
1.
2.
Cao L, Tay FE (2001) Financial forecasting using support vector machines. Neural Comput Appl 10(2):184–192CrossRefMATH
3.
de Carvalho ACPLF, Freitas AA (2009) A tutorial on multi-label classification techniques. In: Abraham A, Hassanien A-E, Snášel V (eds) Foundations of computational intelligence, vol 5. Springer, Berlin, pp 177–195
4.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
5.
6.
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
7.
Ding S, Huang H, Xu X, Wang J (2014) Polynomial smooth twin support vector machines. Appl Math Inf Sci 8(4):2063MathSciNetCrossRef
8.
Ding S, Yu J, Qi B, Huang H (2014) An overview on twin support vector machines. Artif Intell Rev 42(2):245–252CrossRef
9.
Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, LondonMATH
10.
Famoye F, Johnson NL, Kotz S, Balakrishnan N (1995) Continuous univariate distributions, volume 1. Technometrics 37(4):466
11.
Golub GH, Van Loan CF (2012) Matrix computations, vol 3. JHU Press, BaltimoreMATH
12.
Hao PY (2010) New support vector algorithms with parametric insensitive/margin model. Neural Netw 23(1):60–73CrossRef
13.
Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70
14.
15.
Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910CrossRefMATH
16.
Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Machine learning: ECML-98. ECML 1998. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1398. Springer, Berlin, pp 137–142
17.
Khemchandani R, Saigal P (2015) Color image classification and retrieval through ternary decision structure based multi-category twsvm. Neurocomputing 165:444–455CrossRef
18.
Khemchandani R, Sharma S (2016) Robust least squares twin support vector machine for human activity recognition. Appl Soft Comput 47:33–46CrossRef
19.
Kreßel UH-G (1999) Pairwise classification and support vector machines. In: Schölkopf B, Burges C, Smola A (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 255–268
20.
Lei H, Govindaraju V (2005) Half-against-half multi-class support vector machines. In: International workshop on multiple classifier systems. Springer, pp 156–164
21.
Manosha Chathuramali K, Rodrigo R (2012) Faster human activity recognition with svm. In: 2012 International Conference on advances in ICT for emerging regions (ICTer). IEEE, pp 197–203
22.
Milgram J, Cheriet M, Sabourin R (2006) One against one or one against all: which one is better for handwriting recognition with svms? In: Tenth international workshop on frontiers in handwriting recognition. Suvisoft
23.
Musicant D (1998) Ndc: normally distributed clustered datasets. Computer Sciences Department, University of Wisconsin, Madison
24.
Peng X (2010) A \(\nu \)-twin support vector machine (\(\nu \)-tsvm) classifier and its geometric algorithms. Inf Sci 180(20):3863–3875MathSciNetCrossRefMATH
25.
Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10–11):2678–2692CrossRefMATH
26.
Schlkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245CrossRef
27.
Schölkopf B, Tsuda K, Vert JP (2004) Kernel methods in computational biology. MIT press, Cambridge
28.
Shao YH, Chen WJ, Huang WB, Yang ZM, Deng NY (2013) The best separating decision tree twin support vector machine for multi-class classification. Procedia Comput Sci 17:1032–1038CrossRef
29.
Shao YH, Wang Z, Chen WJ, Deng NY (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39(3):451–464CrossRef
30.
31.
32.
Tomar D, Agarwal S (2015) A comparison on multi-class classification methods based on least squares twin support vector machine. Knowl Based Syst 81:131–147CrossRef
33.
34.
Wang Z, Shao YH, Wu TR (2014) Proximal parametric-margin support vector classifier and its applications. Neural Comput Appl 24(3–4):755–764CrossRef
35.
Xu Y, Yang Z, Pan X (2017) A novel twin support-vector machine with pinball loss. IEEE Trans Neural Netw Learn Syst 28(2), 359–370
36.
Yang Z, Xu Y (2016) Laplacian twin parametric-margin support vector machine for semi-supervised classification. Neurocomputing 171:325–334CrossRef
37.
Yang ZM, Hua XY, Shao YH, Ye YF (2016) A novel parametric-insensitive nonparallel support vector machine for regression. Neurocomputing 171:649–663CrossRef
38.
Yang ZX, Shao YH, Zhang XS (2013) Multiple birth support vector machine for multi-class classification. Neural Comput Appl 22(1):153–161CrossRef
39.
Zhang X, Ding S, Sun T (2016) Multi-class lstmsvm based on optimal directed acyclic graph and shuffled frog leaping algorithm. Int J Mach Learn Cybern 7(2):241–251CrossRef
40.
Zhang X, Ding S, Xue Y (2017) An improved multiple birth support vector machine for pattern classification. Neurocomputing 225:119–128CrossRef
Metadaten
Titel
Robust Parametric Twin Support Vector Machine for Pattern Classification
verfasst von
Reshma Rastogi
Sweta Sharma
Suresh Chandra
Publikationsdatum
16.05.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9633-3

Weitere Artikel der Ausgabe 1/2018

Neural Processing Letters 1/2018 Zur Ausgabe

OriginalPaper

Chebyshev Functional Link Artificial Neural Network Based on Correntropy Induced Metric

OriginalPaper

Synchronization of a Class of Improved Neural Networks Based on Periodic Intermittent Control

OriginalPaper

A Capable Neural Network Framework for Solving Degenerate Quadratic Optimization Problems with an Application in Image Fusion

OriginalPaper

A Feature Selection Method for Projection Twin Support Vector Machine

OriginalPaper

Mittag–Leffler Stability and Global Asymptotically -Periodicity of Fractional-Order BAM Neural Networks with Time-Varying Delays

OriginalPaper

Leaf Recognition Based on DPCNN and BOW

Neuer Inhalt

    Bildnachweise