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Support Vector Clustering
A novel clustering method using the approach of support vector machines, where data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where the minimal enclosing sphere is searched for.
On the computational power of neural nets
It is proved that one may simulate all Turing Machines by rational nets in linear time, and there is a net made up of about 1,000 processors which computes a universal partial-recursive function.
Neural networks and analog computation - beyond the Turing limit
- H. Siegelmann
- Computer ScienceProgress in theoretical computer science
- 1 March 1999
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.
Posttranscriptional Regulation of BK Channel Splice Variant Stability by miR-9 Underlies Neuroadaptation to Alcohol
Analog computation via neural networks
The authors pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research, which exhibit at least some robustness with respect to noise and implementation errors.
Computation Beyond the Turing Limit
- H. Siegelmann
- 28 April 1995
A simply described but highly chaotic dynamical system called the analog shift map is presented here, which has computational power beyond the Turing limit (super-Turing); it computes exactly like neural networks and analog machines.
Turing computability with neural nets
Computational capabilities of recurrent NARX neural networks
- H. Siegelmann, B. Horne, C. Lee Giles
- Computer ScienceIEEE Trans. Syst. Man Cybern. Part B
- 1 April 1997
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.
A support vector clustering method
- A. Ben-Hur, H. Siegelmann, D. Horn, V. Vapnik
- Computer ScienceProceedings 15th International Conference on…
- 3 September 2000
We present a novel kernel method for data clustering using a description of the data by support vectors. The kernel reflects a projection of the data points from data space to a high dimensional…
Brain-inspired replay for continual learning with artificial neural networks
A replay-based algorithm for deep learning without the need to store data is proposed in which internal or hidden representations are replayed that are generated by the network’s own, context-modulated feedback connections, and it provides a novel model for replay in the brain.