skip to main content
article

Review of "Learning Kernel Classifiers: Theory and Algorithms by Ralf Herbrich." MIT Press, Cambridge, Mass., 2002. ISBN 026208306X, 384 pages; and Review of "Learning with Kernels: Support Vector Machines, Regularization Optimization and Beyond by Bernhard Scholkopf and Alexander J. Smola." IT Press, Cambridge, Mass., 2002, ISBN 0262194759, 644 pages.

Published: 01 September 2004 Publication History

Abstract

Pattern recognition is arguably the critical first step in intelligence - be it natural or artificial. Science could not exist if humans were not able to spot regularities. Only subsequently do we analyze and classify them, and ultimately come up with underlying descriptions, some of which eventually make it to the lofty status of natural laws. Of course evolution did NOT ENDOw humans (or animals for that matter) with the ability to recognize patterns so we could build grandiose scientific edifices, rather we need that ability at the most elementary level. Humans simply cannot function in everyday life without pattern recognition capabilities and when those capabilities are impaired, as in patients with Alzheimer's disease for example, the result is nothing less than tragic.

References

[1]
T. M. Mitchell, Machine Learning McGraw-Hill, New York, 1997.
[2]
R. O. Duda, and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
[3]
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, (2nd Edition), Wiley, New York, 2000.
[4]
V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
[5]
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, 2000.
[6]
T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer Verlag, Berlin, 2001.
[7]
V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge, 2001.

Cited By

View all
  • (2023)Unsupervised Keyphrase Extraction from Scientific PublicationsComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-24337-0_16(215-229)Online publication date: 26-Feb-2023
  • (2018)Comparison of Inductive Inference Mechanisms and their Suitability for an Information Model for the Visualization of UncertaintyApplied Mechanics and Materials10.4028/www.scientific.net/AMM.885.147885(147-155)Online publication date: Nov-2018
  • (2014)Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm ModelAdvanced Materials Research10.4028/www.scientific.net/AMR.998-999.1033998-999(1033-1036)Online publication date: Jul-2014
  • Show More Cited By
  1. Review of "Learning Kernel Classifiers: Theory and Algorithms by Ralf Herbrich." MIT Press, Cambridge, Mass., 2002. ISBN 026208306X, 384 pages; and Review of "Learning with Kernels: Support Vector Machines, Regularization Optimization and Beyond by Bernhard Scholkopf and Alexander J. Smola." IT Press, Cambridge, Mass., 2002, ISBN 0262194759, 644 pages.

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM SIGACT News
    ACM SIGACT News  Volume 35, Issue 3
    September 2004
    78 pages
    ISSN:0163-5700
    DOI:10.1145/1027914
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 September 2004
    Published in SIGACT Volume 35, Issue 3

    Check for updates

    Qualifiers

    • Article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 05 Mar 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Unsupervised Keyphrase Extraction from Scientific PublicationsComputational Linguistics and Intelligent Text Processing10.1007/978-3-031-24337-0_16(215-229)Online publication date: 26-Feb-2023
    • (2018)Comparison of Inductive Inference Mechanisms and their Suitability for an Information Model for the Visualization of UncertaintyApplied Mechanics and Materials10.4028/www.scientific.net/AMM.885.147885(147-155)Online publication date: Nov-2018
    • (2014)Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm ModelAdvanced Materials Research10.4028/www.scientific.net/AMR.998-999.1033998-999(1033-1036)Online publication date: Jul-2014
    • (2014)Monitoring Nonlinear Batch Process Using Statis-Based MethodApplied Mechanics and Materials10.4028/www.scientific.net/AMM.518.350518(350-355)Online publication date: Feb-2014
    • (2012)A l1-minimization Based Approach for Hyperspectral Data ClassificationKey Engineering Materials10.4028/www.scientific.net/KEM.500.675500(675-681)Online publication date: Jan-2012
    • (2012)Engineering Design Based on Hammersley Sequences Sampling Method and SVRAdvanced Materials Research10.4028/www.scientific.net/AMR.544.206544(206-211)Online publication date: Jun-2012

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media