Dear Connectionists,
I would like to announce the availability of papers
dealing with theoretical and practical aspects of fractal
representations of (possibly very long and complex) symbolic sequences
via iterative function systems.
1. P. Tino: Spatial Representation of Symbolic Sequences through
Iterative Function Systems.
IEEE Transactions on Systems, Man, and Cybernetics Part A:
Systems and Humans. 1999.
- A theoretical study connecting multifractal properties of
such representations with
entropic measures on symbolic sequences
2. P. Tino, G. Dorffner: Predicting the future of discrete sequences
from fractal representations of the past.
Machine Learning, accepted.
- Mostly empirical study of predictive models constructed on
fractal representations. Predictive models are closely
related to variable memory length Markov models.
3. P. Tino, G. Dorffner, Ch. Schittenkopf: Understanding State Space
Organization in Recurrent
Neural Networks with Iterative Function Systems Dynamics.
In Hybrid Neural Symbolic Integration, 2000.
- A connection between recurrent nets and fractal
representations.
4. P. Tino, M. Koteles: Extracting finite state representations from
recurrent neural networks trained
on chaotic symbolic sequences.
IEEE Transactions on Neural Networks, 1999.
- Contains an example of using fractal representations to
monitor the processes of training recurrent nets
and extracting knowledge from trained nets.
The papers can be downloaded from
http://www.ncrg.aston.ac.uk/~tinop/my.publ.html
Also available are some minor applications of this methodology in
finance and natural language
modeling.
Best regards,
Peter T.
--
Peter Tino - Neural Computing Research Group
Aston University, Aston Triangle, Birmingham, B4 7ET, UK
(+44 (0)121) 359 3611 ext. 4285, fax: 333 6215
http://www.ncrg.aston.ac.uk/~tinop/