Large Margin Rank Boundaries for Ordinal Regression

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ADVANCES IN

LARGE MARGIN

CLASSIFIERS

EDITED BY

ALEXANDER J. SMOLA

PETER L. BARTLETT

BERNHARD SCHÖLKOPF

DALE SCHUURMANS

The concept of large margins is a unifying principle

for the analysis of many different approaches to the

classification of data from examples, including boost-

ing, mathematical programming, neural networks,

and support vector machines. The fact that it is the

margin, or confidence level, of a classification—that

is, a scale parameter—rather than a raw training er-

ror that matters has become a key tool for dealing

with classifiers. This book shows how this idea ap-

plies to both the theoretical analysis and the design

of algorithms.

The book provides an overview of recent develop-

ments in large margin classifiers, examines connec-

tions with other methods (e.g., Bayesian inference),

and identifies strengths and weaknesses of the

method, as well as directions for future research.

Among the contributors are Manfred Opper,

Vladimir Vapnik, and Grace Wahba.

Alexander J. Smola is a researcher in the Depart-

ment of Engineering and RSISE, Australian National

University. Peter L. Bartlett is Senior Fellow, Com-

puter Sciences Laboratory, Australian National Uni-

versity. Bernhard Schölkopf is a researcher

at Microsoft Research Ltd., Cambridge, UK.

Dale Schuurmans is Assistant Professor of

Computer Science at the University of Waterloo,

Ontario, Canada.

OF RELATED INTEREST

Advances in Kernel Methods

Support Vector Learning

edited by Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola

The Support Vector Machine is a powerful new learning algorithm for solving a

variety of learning and function estimation problems, such as pattern

recognition, regression estimation, and operator inversion.

The impetus for this collection was a workshop on Support

Vector Machines held at the 1997 NIPS conference. The

contributors, both university researchers and engineers

developing applications for the corporate world,

form a Who’s Who of this exciting new area.

JACKET IMAGE:

ADVANCES IN LARGE MARGIN CLASSIFIERS

SMOLA

BARTLETT

SCHÖLKOPF

SCHUURMANS

EDITORS

ADVANCES IN

LARGE MARGIN

CLASSIFIERS

NEURAL INFORMATION PROCESSING SERIES

edited by Alexander J. Smola, Peter J. Bartlett,

Bernhard Schölkopf, and Dale Schuurmans

Pulsed Neural Networks

edited by Wolfgang Maass and Christopher M. Bishop

Most practical applications of artificial neural networks are based on a

computational model involving the propagation of continuous vari-

ables from one processing unit to the next. In recent years, data

from neurobiological experiments have made it increasingly

clear that biological neural networks, which communicate

through pulses, use the timing of the pulses to transmit infor-

mation and perform computation. This realization has stim-

ulated significant research on pulsed neural networks,

including theoretical analyses and model development, neu-

robiological modeling, and hardware implementation. This

book presents the complete spectrum of current research in

pulsed neural networks and includes the most important

work from many of the key scientists in the field.

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The MIT Press

Massachusetts Institute of Technology

Cambridge, Massachusetts 02142

http://mitpress.mit.edu

SMOAH 0-262-19448-1

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