Connectionist Approaches to Language Learning (Paperback, Softcover reprint of the original 1st ed. 1991)

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-Verlag New York
Country of origin:	United States
Series:	The Springer International Series in Engineering and Computer Science, 154
Release date:	October 2012
First published:	1991
Editors:	David Touretzky
Dimensions:	235 x 155 x 8mm (L x W x T)
Format:	Paperback
Pages:	149
Edition:	Softcover reprint of the original 1st ed. 1991
ISBN-13:	978-1-4613-6792-5
Categories:	Books > Computing & IT > General theory of computing > General Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	1-4613-6792-1
Barcode:	9781461367925

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