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MRA Kernel Matching Pursuit Machine

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PRICAI 2006: Trends in Artificial Intelligence (PRICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4099))

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Abstract

Kernel Matching Pursuit Machine (KMPM) is a relatively new learning algorithm utilizing Mercer kernels to produce non-linear version of conventional supervised and unsupervised learning algorithm. But the commonly used Mercer kernels can’t expand a set of complete bases in the feature space (subspace of the square and integrable space). Hence the decision-function found by the machine can’t approximate arbitrary objective function in feature space as precise as possible. Multiresolution analysis (MRA) shows promise for both nonstationary signal approximation and pattern recognition, so we combine KMPM with multiresolution analysis technique to improve the performance of the machine, and put forward a MRA shift-invariant kernel, which is a Mercer admissive kernel by theoretical analysis. An MRA kernel matching pursuit machine (MKMPM) is constructed in this paper by Shannon MRA shift-invariant kernel. It is shown that MKMPM is much more effective in the problems of regression and pattern recognition by a large number of comparable experiments.

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References

  1. Engel, Y., Mannor, S., Meir, R.: The kernel recursive least-squares algorithm. IEEE Trans. Signal Processing 52(8), 2275–2285 (2004)

    Article  MathSciNet  Google Scholar 

  2. Vincent, P., Bengio, Y.: Kernel matching pursuit. Machine Learning 48, 165–187 (2002)

    Article  MATH  Google Scholar 

  3. Davis, G., Mallat, S., Zhang, Z.: Adaptive time-frequency decompositions. Optical Engineering 33(7), 2183–2191

    Google Scholar 

  4. Mallat, S.: A theory for nuliresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Machine Intell. 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  5. Bastys, A.: Periodic shift-invariant multiresolution analysis. In: IEEE, Digital Signal Processing Workshop Proceedings, pp. 398–400 (1996)

    Google Scholar 

  6. Zhang, L., Zhou, W.D., Jiao, L.C.: Wavelet support vector machine. IEEE Trans. On Systems, Man, and Cybernetics. Part B: Cybernetics. 34(1) (February 2004)

    Google Scholar 

  7. Kearns, M., Ron, D.: Algorithmic stability and sanity-check bounds for leave-one-out cross validation. In: Proc. Tenth Conf. Comput. Learning Theory., pp. 152–162. ACM Press, New York (1997)

    Chapter  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Li, Q., Jiao, L., Yang, S. (2006). MRA Kernel Matching Pursuit Machine. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_128

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  • DOI: https://doi.org/10.1007/978-3-540-36668-3_128

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36667-6

  • Online ISBN: 978-3-540-36668-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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