arise automatically as a result of the recursive structure of the
task and the continuous nature of the SRN's state space. Elman also
introduces a new graphical technique for study ing network behavior
based on principal components analysis. He shows that sentences
with multiple levels of embedding produce state space trajectories
with an intriguing self similar structure. The development and
shape of a recurrent network's state space is the subject of
Pollack's paper, the most provocative in this collection. Pollack
looks more closely at a connectionist network as a continuous
dynamical system. He describes a new type of machine learning
phenomenon: induction by phase transition. He then shows that under
certain conditions, the state space created by these machines can
have a fractal or chaotic structure, with a potentially infinite
number of states. This is graphically illustrated using a
higher-order recurrent network trained to recognize various regular
languages over binary strings. Finally, Pollack suggests that it
might be possible to exploit the fractal dynamics of these systems
to achieve a generative capacity beyond that of finite-state
machines."
General
Imprint: |
Springer
|
Country of origin: |
Netherlands |
Series: |
The Springer International Series in Engineering and Computer Science, 154 |
Release date: |
September 1991 |
First published: |
1991 |
Editors: |
David Touretzky
|
Dimensions: |
235 x 155 x 11mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
149 |
Edition: |
Reprinted from Machine Learning, Volume 7:2/3 |
ISBN-13: |
978-0-7923-9216-3 |
Categories: |
Books >
Computing & IT >
Applications of computing >
Artificial intelligence >
Machine learning
|
LSN: |
0-7923-9216-7 |
Barcode: |
9780792392163 |
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