Skip to main content

Influence of Positive Instances on Multiple Instance Support Vector Machines

  • Conference paper
  • First Online:
Robot 2015: Second Iberian Robotics Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 418))

  • 2666 Accesses

Abstract

This work studies the influence of the percentage of positive instances on positive bags on the performance of multiple instance learning algorithms using support vector machines. There are several studies that compare the performance of different types of multiple instance learning algorithms in different datasets and the performance of these algorithms with the supervised learning counterparts. Nonetheless, none of them study the influence of having a low or high percentage of positive instances on the data that the classifiers are using to learn. Therefore, we have created a new image dataset with different percentages of positive instances from a dataset for pedestrian detection. Experimental results of the performance of mi-SVM and MI-SVM algorithms on an image annotation task are presented. The results show that higher percentages of positive instances increase the overall accuracy of classifiers based on the maximum bag margin formulation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Amores, J.: Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence 201, 81–105 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  2. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 561–568 (2002)

    Google Scholar 

  3. Babenko, B.: Multiple instance learning: algorithms and applications. View Article PubMed/NCBI Google Scholar (2008)

    Google Scholar 

  4. Bishop, C.M.: Pattern recognition and machine learning. Springer (2006)

    Google Scholar 

  5. Bunescu, R.C., Mooney, R.J.: Multiple instance learning for sparse positive bags. In: Proceedings of the 24th International Conference on Machine Learning, pp. 105–112. ACM (2007)

    Google Scholar 

  6. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)

    Article  Google Scholar 

  7. Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1), 31–71 (1997)

    Article  MATH  Google Scholar 

  8. Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC, vol. 2, p. 5 (2009)

    Google Scholar 

  9. Figueira, D., Taiana, M., Nambiar, A., Nascimento, J., Bernardino, A.: The hda+ data set for research on fully automated re-identification systems. In: 2014 Workshops Computer Vision-ECCV, pp. 241–255. Springer (2014)

    Google Scholar 

  10. Foulds, J., Frank, E.: A review of multi-instance learning assumptions. The Knowledge Engineering Review 25(01), 1–25 (2010)

    Article  Google Scholar 

  11. Gärtner, T., Flach, P.A., Kowalczyk, A., Smola, A.J.: Multi-instance kernels. In: ICML, vol. 2, pp. 179–186 (2002)

    Google Scholar 

  12. Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learnin (2009)

    Google Scholar 

  13. Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. Advances in Neural Information Processing Systems, 570–576 (1998)

    Google Scholar 

  14. Murphy, K.P.: Machine learning: a probabilistic perspective. MIT press (2012)

    Google Scholar 

  15. Nambiar, A., Taiana, M., Figueira, D., Nascimento, J.C., Bernardino, A.: A multi-camera video dataset for research on high-definition surveillance. International Journal of Machine Intelligence and Sensory Signal Processing 1(3), 267–286 (2014)

    Article  Google Scholar 

  16. Ray, S., Craven, M.: Supervised versus multiple instance learning: an empirical comparison. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 697–704. ACM (2005)

    Google Scholar 

  17. Zhang, Q., Goldman, S.A.: EM-DD: an improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems, pp. 1073–1080 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nuno Barroso Monteiro .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Monteiro, N.B., Barreto, J.P., Gaspar, J. (2016). Influence of Positive Instances on Multiple Instance Support Vector Machines. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27149-1_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27148-4

  • Online ISBN: 978-3-319-27149-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics