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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.
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Volume 35 ,  Issue 3  (September 2004) table of contents
REVIEWS: Book reviews table of contents
Pages: 13 - 17  
Year of Publication: 2004
ISSN:0163-5700
Author
Luc T. Wille  Florida Atlantic Univ. Boca Raton, FL
Publisher
ACM  New York, NY, USA
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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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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R. O. Duda, and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
 
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T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning. Springer Verlag, Berlin, 2001.
 
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