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.

Authors:
Luc T. Wille
Affiliations:
Florida Atlantic Univ. Boca Raton, FL
Venue:
ACM SIGACT News
Year:
2004

Citing 5
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning

Machine Learning
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)

Quantified Score

Hi-index	0.00

Visualization

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.