Here's a survey paper on the family of algorithms broadly known
as boosting methods.
[Postscript]
[PDF]
This paper describes a particular variant of boosting developed for
document classification. In such applications, there is a huge number
of potential attributes or factors from which one may want to do prediction,
but most of them will be irrelevant to any particular example.
[Postscript]
[PDF]
This one describes an application of boosting-like methods to a
simulated auction scenario. I haven't read this one myself yet, but
thought it might hit closer to home.
[Postscript]
[PDF]
Here's a paper on so-called multiplicative update methods, which
also often work well with huge numbers of features...
[Postscript]
[PDF]
...and here is an application of such methods to portfolio selection.
[Postscript]
[PDF]
Here is a tutorial on Support Vector Machines (SVMs), a different
approach to problems with many features/factors. Only have it in
PDF, and haven't read it myself yet.
[PDF]
This one is a bit of an outlier, but it's an attempted application
to the later stuff I talked about (trying to use Markov decision processes
to model one's own effects on the environment/market) to automated
market-making.
[Postscript]
[PDF]
POSTED 4/15/02: The following two papers were posted following a conversation with Alex on different methods of limiting complexity to avoid overfitting when learning a probability distribution from sample data.
This one emphasizes complexity regularization in classification problems,
but every aspect has an analogue in the distribution-learning setting.
[Postscript]
[PDF]
This one develops the tools needed to develop complexity regularization
methods for distribution learning.
[Postscript]
[PDF]
POSTED 4/15/02: Some ECN data files:
QQQ
QQQ2
YHOO
IBM
POSTED 6/5/02: Papers related to conversation with Jeff W. and Michael B. on gradient methods for searching in a parametric stratgegy space
[BaxterBartlett, Postscript] [BaxterBartlett, PDF]
[BaxterWeaverBartlett, Postscript] [BaxterWeaverBartlett, PDF]
[BaxterBartlett2, Postscript] [BaxterBartlett2, PDF]
[Sutton et al., Postscript] [Sutton et al., PDF]
[Kearns et al., Postscript] [Kearns et al., PDF] (Mainly appendix B)