Connectionist Approaches to Language Learning

David Touretzky (Curatore)

Springer-Verlag New York Inc., 2012

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.

• Curatore: David Touretzky
• Editore: Springer-Verlag New York Inc.
• Collana: The Springer International Series in Engineering and Computer Science
• Anno: 2012
• Rilegatura: Paperback / softback
• Pagine: 149 p.
• Dimensioni: 235 x 155 mm
• EAN: 9781461367925

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