Skip to main content
SpringerLink
Log in

Multiple Instance Learning with Radial Basis Function Neural Networks

  • Conference paper
Neural Information Processing (ICONIP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3316))

Included in the following conference series:

Abstract

This paper investigates the application of radial basis function neural networks (RBFNs) for solving the problem of multiple instance learning (MIL). As a particular form of the traditional supervised learning paradigm, MIL deals with the classification of patterns grouped into bags. Labels of bags are known but not those of individual patterns. To solve the MIL problem, a neural solution based on RBFNs is proposed. A classical application of RBFNs and bag unit-based variants are discussed. The evaluation, conducted on two benchmark data sets, showed that the proposed bag unit-based variant performs very well.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

References

  1. Andrews, S., Hofmann, T., Tsochantaridis, I.: Multiple Instance Learning with Generalized Support Vector Machines. In: Proc. of 14th Nat. Conf. on A.I, pp. 943–944 (2002)

    Google Scholar 

  2. Auer, P.: On learning from Multi-instance Examples: Empirical Evaluation of a Theoretical Approach. In: Proc. of 14th Int. Conf. on Machine Learning, pp. 21–29 (1997)

    Google Scholar 

  3. Chiu, S.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems 2(3) (1994)

    Google Scholar 

  4. Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the Multiple Instance Problem with Axis-parallel Rectangles. Artificial Intelligence 89, 31–71 (1997)

    Article  MATH  Google Scholar 

  5. Maron, O., Ratan, A.: Multiple-Instance Learning for Natural Scene Classification. In: Proc. of 15th Int. Conf. on Machine Learning, pp. 341–349 (1998)

    Google Scholar 

  6. Moody, J., Darken, C.: Fast Learning in Networks of locally-tuned Processing Units. Neural Computation 1, 284–294 (1989)

    Article  Google Scholar 

  7. Ramon, J., De Raedt, L.: Multi-instance Neural Networks. In: Proc. of the Int. Conf. on Machine Learning Workshop on Attribute-Value and Relational Learning (2000)

    Google Scholar 

  8. Ray, S., Page, O.: Multiple Instance Regression. In: Proc. of 18th Int. Conf. on Machine Learning, pp. 425–432 (2001)

    Google Scholar 

  9. Wang, J., Zucker, J.: Solving Multiple-Instance Problem: A Lazy Learning Approach. In: Proc. of 17th Int. Conf. on Machine Learning, pp. 1119–1125 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Informatics-Systems, University of Klagenfurt, Universitätsstrasse 65, A-9020, Klagenfurt, Austria

    Abdelhamid Bouchachia

Authors
  1. Abdelhamid Bouchachia
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Electronics and Communication Sciences Unit, Kolkata, India

    Nikhil Ranjan Pal

  2. School of Computer and Information Sciences, Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Private Bag 92006, Auckland, New Zealand

    Nik Kasabov

  3. Department of Instrumentation and Electronics Engineering, Jadavpur University, Salt-lake Campus, 700098, Calcutta, India

    Rajani K. Mudi

  4. Indian Statistical Institute, 203 B. T. Road, 700 108, Calcutta,  

    Srimanta Pal

  5. Indian Statistical Institute, Computer Vision and Pattern Recognition Unit, 700108, Kolkata, India

    Swapan Kumar Parui

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bouchachia, A. (2004). Multiple Instance Learning with Radial Basis Function Neural Networks. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_67

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30499-9_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation