The Path Kernel

Andrea Baisero, Florian T. Pokorny, Danica Kragic, Carl Henrik Ek

Abstract

Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data. In this paper, we present a new kernel for encoding sequential data. We present our results comparing the proposed kernel to the state of the art, showing a significant improvement in classification and a much improved robustness and interpretability.

References

  1. Bahlmann, C., Haasdonk, B., and Burkhardt, H. (2002). Online handwriting recognition with support vector machines - a kernel approach. In 8th International Workshop on Frontiers in Handwriting Recognition.
  2. Berlinet, A. and Thomas-Agnan, C. (2004). Reproducing kernel Hilbert spaces in probability and.
  3. Buhmann, M. D. and Martin, D. (2003). Radial basis functions: theory and implementations.
  4. Chang, C. C. and Lin, C. J. (2001). LIBSVM: a library for support vector machines.
  5. Cristianini, N. and Shawe-Taylor, J. (2006). An introduction to support Vector Machines: and other kernel-based learning methods.
  6. Cuturi, M. (2010). Fast Global Alignment Kernels. In International Conference on Machine Learning.
  7. Cuturi, M., Vert, J.-P., Birkenes, O., and Matsui, T. (2007). A Kernel for Time Series Based on Global Alighments. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 413-416.
  8. Gudmundsson, S., Runarsson, T. P., and Sigurdsson, S. (2008). Support vector machines and dynamic time warping for time series. IEEE International Joint Conference on Neural Networks, pages 2772-2776.
  9. Haasdonk, B. (2005). Feature space interpretation of SVMs with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4):482-492.
  10. Haasdonk, B. and Burkhardt, H. (2007). Invariant kernel functions for pattern analysis and machine learning. Machine learning, 68(1):35-61.
  11. Haussler, D. (1999). Convolution kernels on discrete structures. Technical report.
  12. Leslie, C. and Kuang, R. (2004). Fast String Kernels using Inexact Matching for Protein Sequences. The Journal of Machine Learning Research, 5:1435-1455.
  13. Li, M. and Zhu, Y. (2006). Image classification via LZ' based string kernel: a comparative study. Advances in knowledge discovery and data mining.
  14. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., and Watkins, C. (2002). Text classification using string kernels. The Journal of Machine Learning Research, 2:419-444.
  15. Luo, G., Bergströ m, N., Ek, C. H., and Kragic, D. (2011). Representing actions with Kernels. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2028-2035.
  16. Sakoe, H. and Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 26(1):43-49.
  17. Saunders, C., Tschach, H., and Shawe-Taylor, J. (2002). Syllables and other string kernel extensions. Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02).
  18. Sivic, J. and Zisserman, A. (2009). Efficient Visual Search of Videos Cast as Text Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4):591-606.
  19. Watkins, C. (1999). Dynamic alignment kernels. In Advances in Neural Information Processing Systems.
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Paper Citation


in Harvard Style

Baisero A., T. Pokorny F., Kragic D. and Henrik Ek C. (2013). The Path Kernel . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 50-57. DOI: 10.5220/0004267300500057


in Bibtex Style

@conference{icpram13,
author={Andrea Baisero and Florian T. Pokorny and Danica Kragic and Carl Henrik Ek},
title={The Path Kernel},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={50-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004267300500057},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - The Path Kernel
SN - 978-989-8565-41-9
AU - Baisero A.
AU - T. Pokorny F.
AU - Kragic D.
AU - Henrik Ek C.
PY - 2013
SP - 50
EP - 57
DO - 10.5220/0004267300500057