Next: About this document ...
Up: SEQUENTIAL NEURAL TEXT COMPRESSION
Previous: VI. ACKNOWLEDGMENT

 1

T. C. Bell, J. G. Cleary, and I. H. Witten.
Text Compression.
Prentice Hall, Englewood Cliffs, NJ, 1990.
 2

G. Held.
Data Compression.
Wiley and Sons LTD, New York, 1991.
 3

S. Lindstädt.
Comparison of two unsupervised neural network models for redundancy
reduction.
In M. C. Mozer, P. Smolensky, D. S. Touretzky, J. L. Elman, and A. S.
Weigend, editors, Proc. of the 1993 Connectionist Models Summer School,
pages 308315. Hillsdale, NJ: Erlbaum Associates, 1993.
 4

D. E. Rumelhart, G. E. Hinton, and R. J. Williams.
Learning internal representations by error propagation.
In Parallel Distributed Processing, volume 1, pages 318362.
MIT Press, 1986.
 5

J. H. Schmidhuber.
Learning complex, extended sequences using the principle of history
compression.
Neural Computation, 4(2):234242, 1992.
 6

J. H. Schmidhuber.
Learning factorial codes by predictability minimization.
Neural Computation, 4(6):863879, 1992.
 7

J. H. Schmidhuber.
Learning unambiguous reduced sequence descriptions.
In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors, Advances in Neural Information Processing Systems 4, pages 291298. San
Mateo, CA: Morgan Kaufmann, 1992.
 8

J. H. Schmidhuber, M. C. Mozer, and D. Prelinger.
Continuous history compression.
In H. Hüning, S. Neuhauser, M. Raus, and W. Ritschel, editors,
Proc. of Intl. Workshop on Neural Networks, RWTH Aachen, pages 8795.
Augustinus, 1993.
 9

J. H. Schmidhuber and D. Prelinger.
Discovering predictable classifications.
Neural Computation, 5(4):625635, 1993.
 10

P. J. Werbos.
Beyond Regression: New Tools for Prediction and Analysis in the
Behavioral Sciences.
PhD thesis, Harvard University, 1974.
 11

I. H. Witten, R. M. Neal, and J. G. Cleary.
Arithmetic coding for data compression.
Communications of the ACM, 30(6):520540, 1987.
 12

A. Wyner and J. Ziv.
Fixed data base version of the LempelZiv data compression
algorithm.
IEEE Transactions Information Theory, 37:878880, 1991.
 13

J. Ziv and A. Lempel.
A universal algorithm for sequential data compression.
IEEE Transactions on Information Theory, IT23(5):337343,
1977.
Jürgen Schmidhuber was born January 17, 1963.
He received his diploma in computer science in 1987,
his Ph.D. degree in 1991,
and his postdoctoral degree (Habilitation) in 1993,
all from
Technische Universität München.
Between 1991 and 1993 he worked as a
postdoctoral fellow at
the University of Colorado at Boulder.
Currently he is research director at IDSIA,
a machine learning research institute in
Lugano, Switzerland.
He published about 70 papers on
supervised, unsupervised, and reinforcement
learning algorithms for neural networks.
A current focus of research is on
Kolmogorov complexity theory and its
application to machine learning.
Stefan Heil was born March 21, 1968.
He received his diploma degree in 1995
from Technische Universität München.
In 1994 he spent two months at the Department
of Computer Science, California State University,
Fresno. He is very tall.
Juergen Schmidhuber
20030213