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
Published in:
General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Hyper-Parameter Tuning for Support Vector Machines by Estimation of Distribution Algorithms

Authors : Luis Carlos Padierna, Martín Carpio, Alfonso Rojas, Héctor Puga, Rosario Baltazar, Héctor Fraire

Published in: Nature-Inspired Design of Hybrid Intelligent Systems

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Hyper-parameter tuning for support vector machines has been widely studied in the past decade. A variety of metaheuristics, such as Genetic Algorithms and Particle Swarm Optimization have been considered to accomplish this task. Notably, exhaustive strategies such as Grid Search or Random Search continue to be implemented for hyper-parameter tuning and have recently shown results comparable to sophisticated metaheuristics. The main reason for the success of exhaustive techniques is due to the fact that only two or three parameters need to be adjusted when working with support vector machines. In this chapter, we analyze two Estimation Distribution Algorithms, the Univariate Marginal Distribution Algorithm and the Boltzmann Univariate Marginal Distribution Algorithm, to verify if these algorithms preserve the effectiveness of Random Search and at the same time make more efficient the process of finding the optimal hyper-parameters without increasing the complexity of Random Search.

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
previous chapter Comparing Grammatical Evolution’s Mapping Processes on Feature Generation for Pattern Recognition Problems
next chapter Viral Analysis on Virtual Communities: A Comparative of Tweet Measurement Systems
Footnotes
Literature
1.
N. Christianini and J. Shawe-Taylor. An Introduction to SVM and other Kernel Based Methods. Cambridge, U.K.: Cambridge University Press, 2000.
2.
V. Vapnik, Statistical Learning Theory, New York: John Wiley and Sons, 1998.
3.
M. Kanevski, V. Timonin and A. Pozdnukhov, Machine Learning for Spatial Environmental Data: theory, applications and software, CRC Press, 2009.
4.
O. Chapelle, V. Vapnik, O. Bousquet and S. Mukherjee, Choosing Multiple Parameters for Support Vector Machines Machine Learning, vol. 46 (1-3), pp. 131-159, 2002.
5.
F. Friedrichs and C. Igel. Evolutionary tuning of multiple SVM parameters. Neurocomputing, vol 64 pp. 107-117, 2005.
6.
C. Gold, A. Holub y P. Sollich. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Networks, vol 18 (5) pp. 693-701, 2005.
7.
C.-L. Huang and W. Chieh-Jen. A GA-based feature selection and parameters optimization for support vector machines. Expert System with Applications, vol 31(2) pp. 231-240, 2006.
8.
S.-W. Lin, K.-C. Ying y S.-C. L. Z.-J. Chen. Particle Swarm Optimization for Parameter Determination and Feature Selection of Support Vector Machines. Expert System with Applications, vol 35(4) pp. 1817-1824, 2008.
9.
J. Bergstra and Y. Bengio. Random Search for Hyper-Parameter Optimization. Journal of Machine Learning Research, 13(1) pp. 281-305, 2012.
10.
R. Mantovani, A. Rossi, J. Vanschoren and B. d.-C. A. Bischl. Effectiveness of Random Search in SVM hyper-parameter tuning. The International Joint Conference on Neural Networks (IJCNN), 2015.
11.
A. Shigeo, Support Vector Machines for Pattern Classification, New York: Springer, 2010.
12.
N. Deng, Y. Tian and C. Zhang, Support Vector Machines, Boca Raton: CRC Press, 2013.
13.
M. Hauschild and M. Pelikan. An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation, vol 1(3) pp. 111-128, 2011.
14.
D. Simon, Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence, Hoboken: John Wiley and Sons, 2013.
15.
S. I. Valdez, A. Hernández and S. Botello. A Boltzmann based estimation of distribution algorithm. Information Sciences, vol 236 pp. 126-137, 2013.
Metadata
Title
Hyper-Parameter Tuning for Support Vector Machines by Estimation of Distribution Algorithms
Authors
Luis Carlos Padierna
Martín Carpio
Alfonso Rojas
Héctor Puga
Rosario Baltazar
Héctor Fraire
Copyright Year
2017
Book
Nature-Inspired Design of Hybrid Intelligent Systems

Print ISBN: 978-3-319-47053-5

Electronic ISBN: 978-3-319-47054-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-47054-2_53

Premium Partner

    Image Credits