H
Hava T. Siegelmann
Researcher at University of Massachusetts Amherst
Publications - 188
Citations - 8174
Hava T. Siegelmann is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: Artificial neural network & Recurrent neural network. The author has an hindex of 34, co-authored 172 publications receiving 7092 citations. Previous affiliations of Hava T. Siegelmann include Harvard University & Technion – Israel Institute of Technology.
Papers
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Support vector clustering
TL;DR: In this paper, a Gaussian kernel based clustering method using support vector machines (SVM) is proposed to find the minimal enclosing sphere, which can separate into several components, each enclosing a separate cluster of points.
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On the Computational Power of Neural Nets
TL;DR: It is proved that one may simulate all Turing machines by such nets, and any multi-stack Turing machine in real time, and there is a net made up of 886 processors which computes a universal partial-recursive function.
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Computational capabilities of recurrent NARX neural networks
TL;DR: It is constructively proved that the NARX networks with a finite number of parameters are computationally as strong as fully connected recurrent networks and thus Turing machines, raising the issue of what amount of feedback or recurrence is necessary for any network to be Turing equivalent and what restrictions on feedback limit computational power.
Book
Neural networks and analog computation: beyond the Turing limit
TL;DR: This chapter discusses Neural Networks and Turing Machines, which are concerned with the construction of neural networks based on the explicit specification of a discrete-time Turing machine.
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Turing computability with neural nets
TL;DR: The existence of a finite neural network, made up of sigmoidal neurons, which simulates a universal Turing machine, composed of less than 10 5 synchronously evolving processors, interconnected linearly is shown.