Nothing Special   »   [go: up one dir, main page]

Skip to main content

Dynamics of Incremental Learning by VSF-Network

  • Conference paper
Artificial Neural Networks – ICANN 2009 (ICANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

Included in the following conference series:

Abstract

In this paper, we report the dynamics of VSF-Network. VSF-Network is a neural network for the incremental learning and it is a hybrid neural network combining the chaos neural network with a hierarchical network. VSF-Network can find the unknown elements from input with clusters generated by the chaos neuron. We introduce new incremental learning model to explain the dynamics of VSF-Network in this paper. We show the result of analysis of the dynamics of VSF-Network. In the analysis, we focused on the connection weights between layers and neuron cluster generated by the chaotic behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Cangelosi, A., Hourdakis, E., Tikhanoff, V.: Language acquisition and symbol grounding transfer with neural networks and cognitive robots. In: International Joint Conference on Neural Networks, pp. 2885–2891 (2006)

    Google Scholar 

  2. Giraud-Carrier, C.: A note on the utility of incremental learning. AI Communications 13, 215–223 (2000)

    MATH  Google Scholar 

  3. Kakemoto, Y., Nakasuka, S.: The learning and dynamics of vsf-network. In: Proc. of ISIC 2006(2006)

    Google Scholar 

  4. Aihara, T., Tanabe, T., Toyoda, M.: Chaotic neural networks. Phys. Lett. 144A, 333–340 (1990)

    Article  MathSciNet  Google Scholar 

  5. Tanaka, T., Nakagawa, M.: A study of associative model with chaos neural network. IEICE technical report. Neurocomputing 95(57) (1995)

    Google Scholar 

  6. Kaneko, K.: Chaotic but regular posi-nega switch among coded attractors by cluster size variation. Phys. Rev. Lett. 63, 219 (1989)

    Article  MathSciNet  Google Scholar 

  7. Nozawa, H.: A neural network model as a globally coupled map ans applications based on chaos. CHAOS 2(2), 377–386 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  8. Abe, S.: Chaotic itinerancy on 10gcm. RIMS Kokyuroku of Kyoto University 1244, 1–7 (2002)

    Google Scholar 

  9. Breiman, L.: Bagging predictors. Machine Learning (24), 123–140 (1996)

    Google Scholar 

  10. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kakemoto, Y., Nakasuka, S. (2009). Dynamics of Incremental Learning by VSF-Network. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04274-4_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics