Two new papers in the Journal of Machine Learning Research

[JMLR]

The Journal of Machine Learning Research (www.jmlr.org) is pleased to
announce the availability of two new papers in electronic form:

Tracking the Best Linear Predictor
Mark Herbster and Manfred K. Warmuth
Journal of Machine Learning Research 1(Sep):281-309, 2001

Prior Knowledge and Preferential Structures in Gradient Descent Learning
Algorithms
Robert E. Mahony, Robert C. Williamson
Journal of Machine Learning Research 1(Sep):311-355, 2001


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Tracking the Best Linear Predictor
Mark Herbster and Manfred K. Warmuth
Journal of Machine Learning Research 1(Sep):281-309, 2001

Abstract

In most on-line learning research the total on-line loss of the algorithm is
compared to the total loss of the best off-line predictor u from a
comparison class of predictors. We call such bounds static bounds. The
interesting feature of these bounds is that they hold for an arbitrary
sequence of examples. Recently some work has been done where the predictor
u_t at each trial t is allowed to change with time, and the total on-line
loss of the algorithm is compared to the sum of the losses of u_t at each
trial plus the total ``cost'' for shifting to successive predictors. This is
to model situations in which the examples change over time, and different
predictors from the comparison class are best for different segments of the
sequence of examples. We call such bounds shifting bounds. They hold for
arbitrary sequences of examples and arbitrary sequences of predictors.
Naturally shifting bounds are much harder to prove. The only known bounds
are for the case when the comparison class consists of a sequences of
experts or boolean disjunctions. In this paper we develop the methodology
for lifting known static bounds to the shifting case. In particular we
obtain bounds when the comparison class consists of linear neurons (linear
combinations of experts). Our essential technique is to project the
hypothesis of the static algorithm at the end of each trial into a suitably
chosen convex region. This keeps the hypothesis of the algorithm
well-behaved and the static bounds can be converted to shifting bounds.

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Prior Knowledge and Preferential Structures in Gradient Descent Learning
Algorithms
Robert E. Mahony, Robert C. Williamson
Journal of Machine Learning Research 1(Sep):311-355, 2001

Abstract

A family of gradient descent algorithms for learning linear functions in an
online setting is considered. The family includes the classical LMS
algorithm as well as new variants such as the Exponentiated Gradient (EG)
algorithm due to Kivinen and Warmuth. The algorithms are based on prior
distributions defined on the weight space. Techniques from differential
geometry are used to develop the algorithms as gradient descent iterations
with respect to the natural gradient in the Riemannian structure induced by
the prior distribution. The proposed framework subsumes the notion of
"link-functions".

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These papers and earlier papers in Volume 1 are available electronically at
http://www.jmlr.org in PostScript, PDF and HTML formats; a bound, hardcopy
edition of Volume 1 will be available later this year.

-David Cohn, <david.cohn at acm.org>
  Managing Editor, Journal of Machine Learning Research

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