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Predictive Approaches for Sparse Model Learning

  • Conference paper
Neural Information Processing (ICONIP 2004)

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

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Abstract

In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing parameters. Within certain approximation, we establish equivalence relationships that exist among these approaches. The computational complexity, sparseness and performance on some benchmark data sets are compared with those obtained using relevance vector machine.

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Author information

Authors and Affiliations

  1. Dept of Computer Science and Automation, Indian Institute of Science, Bangalore, 560 012, India

    S. K. Shevade

  2. Philips Innovation Campus, #1, Murphy Road, Bangalore, 560 008, India

    S. Sundararajan

  3. Yahoo! Research Labs, 210 S. DeLacey Avenue, Pasadena, CA, 91105, USA

    S. S. Keerthi

Authors
  1. S. K. Shevade
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  2. S. Sundararajan
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  3. S. S. Keerthi
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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

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© 2004 Springer-Verlag Berlin Heidelberg

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Shevade, S.K., Sundararajan, S., Keerthi, S.S. (2004). Predictive Approaches for Sparse Model Learning. 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_66

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  • DOI: https://doi.org/10.1007/978-3-540-30499-9_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23931-4

  • Online ISBN: 978-3-540-30499-9

  • eBook Packages: Springer Book Archive

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