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Nonlinear Estimation and Classification

  • Book
  • © 2003

Overview

Editors:
  1. David D. Denison

    1. Department of Mathematics, Imperial College, London, UK

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  2. Mark H. Hansen

    1. Room 2C283 Bell Laboratories, Lucent Technologies, Murray Hill, USA

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  3. Christopher C. Holmes

    1. Department of Mathematics, Imperial College, London, UK

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  4. Bani Mallick

    1. Statistical Department, Texas A&M University, College Station, USA

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  5. Bin Yu

    1. Department of Statistics, University of California, Berkeley, Berkeley, USA

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Part of the book series: Lecture Notes in Statistics (LNS, volume 171)

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Table of contents (32 chapters)

  1. Front Matter

    Pages N2-vii
    Download chapter PDF

  2. Introduction

    1. Introduction

      • David D. Denison, Mark H. Hansen, Christopher C. Holmes, Bani Mallick, Bin Yu
      Pages 1-5

  3. Longer Papers

    1. Front Matter

      Pages 7-7
      Download chapter PDF

    2. Wavelet Statistical Models and Besov Spaces

      • Hyeokho Choi, Richard G. Baraniuk
      Pages 9-29

    3. Coarse-to-Fine Classification and Scene Labeling

      • Donald Geman
      Pages 31-48

    4. Environmental Monitoring Using a Time Series of Satellite Images and Other Spatial Data Sets

      • Harri Kiiveri, Peter Caccetta, Norm Campbell, Fiona Evans, Suzanne Furby, Jeremy Wallace
      Pages 49-62

    5. Traffic Flow on a Freeway Network

      • Peter Bickel, Chao Chen, Jaimyoung Kwon, John Rice, Pravin Varaiya, Erik van Zwet
      Pages 63-81

    6. Internet Traffic Tends Toward Poisson and Independent as the Load Increases

      • Jin Cao, William S. Cleveland, Dong Lin, Don X. Sun
      Pages 83-109

    7. Regression and Classification with Regularization

      • Sayan Mukherjee, Ryan Rifkin, Tomaso Poggio
      Pages 111-128

    8. Optimal Properties and Adaptive Tuning of Standard and Nonstandard Support Vector Machines

      • Grace Wahba, Yi Lin, Yoonkyung Lee, Hao Zhang
      Pages 129-147

    9. The Boosting Approach to Machine Learning: An Overview

      • Robert E. Schapire
      Pages 149-171

    10. Improved Class Probability Estimates from Decision Tree Models

      • Dragos D. Margineantu, Thomas G. Dietterich
      Pages 173-188

    11. Gauss Mixture Quantization: Clustering Gauss Mixtures

      • Robert M. Gray
      Pages 189-212

    12. Extended Linear Modeling with Splines

      • Jianhua Z. Huang, Charles J. Stone
      Pages 213-233

  4. Shorter Papers

    1. Front Matter

      Pages 235-235
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    2. Adaptive Sparse Regression

      • Mário A. T. Figueiredo
      Pages 237-247

    3. Multiscale Statistical Models

      • Eric D. Kolaczyk, Robert D. Nowak
      Pages 249-259

    4. Wavelet Thresholding on Non-Equispaced Data

      • Maarten Jansen
      Pages 261-271

    5. Multi-Resolution Properties of Semi-Parametric Volatility Models

      • Enrico Capobianco
      Pages 273-284

    6. Confidence Intervals for Logspline Density Estimation

      • Charles Kooperberg, Charles J. Stone
      Pages 285-295
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Keywords

About this book

Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.

Editors and Affiliations

  • Department of Mathematics, Imperial College, London, UK

    David D. Denison, Christopher C. Holmes

  • Room 2C283 Bell Laboratories, Lucent Technologies, Murray Hill, USA

    Mark H. Hansen

  • Statistical Department, Texas A&M University, College Station, USA

    Bani Mallick

  • Department of Statistics, University of California, Berkeley, Berkeley, USA

    Bin Yu

Bibliographic Information

  • Book Title: Nonlinear Estimation and Classification

  • Editors: David D. Denison, Mark H. Hansen, Christopher C. Holmes, Bani Mallick, Bin Yu

  • Series Title: Lecture Notes in Statistics

  • DOI: https://doi.org/10.1007/978-0-387-21579-2

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2003

  • Softcover ISBN: 978-0-387-95471-4Published: 22 January 2003

  • eBook ISBN: 978-0-387-21579-2Published: 11 November 2013

  • Series ISSN: 0930-0325

  • Series E-ISSN: 2197-7186

  • Edition Number: 1

  • Number of Pages: VII, 477

  • Topics: Statistical Theory and Methods

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