Papers and Publications (see also the
BIBTEX file)
Books
D. Ormoneit.
Probability Estimating Neural Networks,
Shaker Verlag, Doctoral Thesis, Institut für Informatik, Technische
Universität München, 1998.
ISBN 3-8265-3723-8.
You can find a summary here.
Book Chapters
D. Ormoneit and R. Neuneier.
Conditional value at risk.
In Computational Finance. MIT Press, 1999, pages 41-52.
An older version of this paper is available
online.
D. Ormoneit and S. Sen.
Kernel-based reinforcement learning.Machine Learning, 2002.
To appear.
An older technical report is available
here.
D. Ormoneit.
A regularization approach to continuous learning with an application
to financial derivatives pricing.
Neural Networks,
12(10):1405-1412, 1999.
An older version of this work as a working paper is available
here.
D. Ormoneit and H. White.
An efficient algorithm to compute maximum entropy densities.Econometric Reviews, 18(2):127-140, 1999.
The table
of lambda- and mu-values which is mentioned in this paper is
here
and the C++ program for the numerical integration is
here.
D. Ormoneit and V. Tresp.
Averaging, maximum penalized likelihood and Bayesian estimation for
improving Gaussian mixture probability density estimates.IEEE Transactions on Neural Networks, 9(4):639-650, 1998.
Reviewed Conference Articles
C. Lemieux, D. Ormoneit, and David J. Fleet.
Lattice Particle Filters.
In Uncertainty in Artificial Intelligence, 2001.
To appear.
D. Ormoneit, H. Sidenbladh, M. J. Black, and T. Hastie.
Learning and tracking cyclic human motion.
In Advances in Neural Information Processing Systems 13. The
MIT Press, 2001.
D. Ormoneit and T. Hastie.
Optimal kernel shapes for local linear regression.
In S. A. Solla, T. K. Leen, and K-R. Müller, editors, Advances
in Neural Information Processing Systems 12,
pages 540-546. The MIT Press, 2000.
D. Ormoneit and R. Neuneier.
Experiments in predicting the German stock index DAX with density
estimating neural networks.
In Proceedings of the IEEE/IAFE 1996 Conference on
Computational Intelligence in Financial Engineering (CIFEr), pages 66-71,
1996.
D. Ormoneit and S. Sen.
Kernel-Based Reinforcement Learning
Technical Report 1999-8, Department of Statistics, Stanford
University, May 1999.
Forschungsbericht Künstliche Intelligenz, Technische Universität München 1997, FKI-220-97:
An Efficient Algorithm to Compute Maximum Entropy Densities
The table of lambda- and mu-values which is mentioned in this paper is
here
and the C++ program for the numerical integration is here.
D. Ormoneit.
Arbeits- und Ergebnisbericht zum ersten Fortsezungsantrag auf
Weiterführung des Graduiertenkollegs,
Institut für Informatik, Technische Universität
München 1997, Kapitel 1.3.7:
Computational Economies, Wahrscheinlichkeitsdichten und
Neuronale Netze zur Entscheidungsmodellierung in Multiagentensystemen