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
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Model of a Weighted Agent System for Personalised E-Learning Curriculum Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition

verfasst von : Eva Tuba, Romana Capor Hrosik, Adis Alihodzic, Raka Jovanovic, Milan Tuba

Erschienen in: Modelling and Development of Intelligent Systems

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Handwritten digit recognition is an important subarea in the object recognition research area. Support vector machines represent a very successful recent binary classifier. Basic support vector machines have to be improved in order to deal with real-world problems. The introduction of soft margin for outliers and misclassified samples as well as kernel function for non linearly separably data leads to the hard optimization problem of selecting parameters for these two modifications. Grid search which is often used is rather inefficient. In this paper we propose the use of one of the latest swarm intelligence algorithms, the fireworks algorithm, for the support vector machine parameters tuning. We tested our approach on standard MNIST base of handwritten images and with selected set of simple features we obtained better results compared to other approaches from literature.

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
Vorheriges Kapitel A Conceptual Framework for Software Fault Prediction Using Neural Networks
Literatur
1.
Singh, P.K., Sarkar, R., Nasipuri, M.: A study of moment based features on handwritten digit recognition. Appl. Comput. Intell. Soft Comput. 2016, 1–17 (2016)CrossRef
2.
Jagannathan, J., Sherajdheen, A., Deepak, R., Krishnan, N.: License plate character segmentation using horizontal and vertical projection with dynamic thresholding. In: International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), pp. 700–705, March 2013
3.
Babu, U.R., Venkateswarlu, Y., Chintha, A.K.: Handwritten digit recognition using k-nearest neighbour classifier. In: World Congress on Computing and Communication Technologies (WCCCT), pp. 60–65. IEEE (2014)
4.
Desai, A.A.: Gujarati handwritten numeral optical character reorganization through neural network. Pattern Recogn. 43(7), 2582–2589 (2010)MATHCrossRef
5.
6.
Ashiquzzaman, A., Tushar, A.K.: Handwritten Arabic numeral recognition using deep learning neural networks. In: IEEE International Conference on Imaging, Vision & Pattern Recognition, pp. 1–4. IEEE (2017)
7.
Stromatias, E., Neil, D., Galluppi, F., Pfeiffer, M., Liu, S.C., Furber, S.: Live demonstration: handwritten digit recognition using spiking deep belief networks on SpiNNaker. In: IEEE International Symposium on Circuits and Systems (ISCAS), p. 1901. IEEE (2015)
8.
Pang, S., Yang, X.: Deep convolutional extreme learning machine and its application in handwritten digit classification. Comput. Intell. Neurosci. 2016, 1–10 (2016). Article ID 3049632CrossRef
9.
Tuba, E., Tuba, M., Simian, D.: Handwritten digit recognition by support vector machine optimized by bat algorithm. In: Computer Science Research Notes CSRN: Papers from the 24th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2602, pp. 369–376 (2016)
10.
Tuba, E., Bacanin, N.: An algorithm for handwritten digit recognition using projection histograms and SVM classifier. In: 23rd IEEE Telecommunications Forum TELFOR, pp. 464–467 (2015)
11.
Kessab, B.E., Daoui, C., Bouikhalene, B., Fakir, M., Moro, K.: Extraction method of handwritten digit recognition tested on the MNIST database. Int. J. Adv. Sci. Technol. 50(6), 99–110 (2013)
12.
Vamvakas, G., Gatos, B., Perantonis, S.J.: Handwritten character recognition through two-stage foreground sub-sampling. Pattern Recogn. 43(8), 2807–2816 (2010)MATHCrossRef
13.
Ebrahimzadeh, R., Jampour, M.: Efficient handwritten digit recognition based on histogram of oriented gradients and SVM. Int. J. Comput. Appl. 104(9), 10–13 (2014)
14.
Impedovo, S., Mangini, F.M., Barbuzzi, D.: A novel prototype generation technique for handwriting digit recognition. Pattern Recogn. 47(3), 1002–1010 (2014)CrossRef
15.
Cardoso, A., Wichert, A.: Handwritten digit recognition using biologically inspired features. Neurocomputing 99(1), 575–580 (2013)CrossRef
16.
Li, J., Zheng, S., Tan, Y.: The effect of information utilization: introducing a novel guiding spark in the fireworks algorithm. IEEE Trans. Evol. Comput. 21(1), 153–166 (2017)CrossRef
17.
18.
19.
Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26(1), 33–42 (2017)CrossRef
20.
21.
Tuba, M., Bacanin, N., Beko, M.: Fireworks algorithm for RFID network planning problem. In: Proceedings of the 25th International Conference Radioelektronika, pp. 440–444, April 2015
22.
Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: 2013 IEEE Congress on Evolutionary Computation, pp. 2069–2077, June 2013
23.
Zheng, S., Li, J., Janecek, A., Tan, Y.: A cooperative framework for fireworks algorithm. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 27–41 (2017)CrossRef
24.
Li, J., Tan, Y.: Enhancing interaction in the fireworks algorithm by dynamic resource allocation and fitness-based crowdedness-avoiding strategy. In: IEEE Congress on Evolutionary Computation (CEC), pp. 4015–4021, July 2016
25.
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)CrossRef
26.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef
Metadaten
Titel
Support Vector Machine Optimized by Fireworks Algorithm for Handwritten Digit Recognition
verfasst von
Eva Tuba
Romana Capor Hrosik
Adis Alihodzic
Raka Jovanovic
Milan Tuba
Copyright-Jahr
2020
Buch
Modelling and Development of Intelligent Systems

Print ISBN: 978-3-030-39236-9

Electronic ISBN: 978-3-030-39237-6

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-39237-6_13

Premium Partner

    Bildnachweise