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:
© 2000 Photodisc, Inc.
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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louiss0072018-03-05非常不错的资源。本来就是想要其中一篇文章，没想到是一正本。包含很多相关文章~多谢楼主
专业打豆豆2018-04-17不客气哈哈哈
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