Results 221 to 230 of about 15,727 (262)
Some of the next articles are maybe not open access.
1998
Abstract On first thought, modeling networks of neurons would seem to be an enterprise having little in common with modeling a checkersplayer. My own first reaction to Art Samuel’s checkersplayer, as I mentioned earlier, was to think the ideas fascinating but far removed from the study of neural networks.
James J. Buckley, Thomas Feuring
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Abstract On first thought, modeling networks of neurons would seem to be an enterprise having little in common with modeling a checkersplayer. My own first reaction to Art Samuel’s checkersplayer, as I mentioned earlier, was to think the ideas fascinating but far removed from the study of neural networks.
James J. Buckley, Thomas Feuring
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Neural Nets: An Evaluation and a Spreadsheet Implementation
Creativity and Innovation Management, 1996Attitudes to neural nets range from suspicion to uncritical admiration. This paper aims to introduce nets and to evaluate their strengths and weaknesses. The language is non‐technical, but the conceptual treatment is intended to be rigorous. A practical method for implementing a neural net on a spreadsheet is described, and sample results illustrated.
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2001
Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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Conventional neural networks work by changing the synaptical weights between their neurons. New neural nets (NNN) are presented, using the recording of temporal sequences of activity, generated by various patterns in chains of neurons, to store and reproduce those patterns.
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Survival analysis and neural nets
Statistics in Medicine, 1994AbstractWe consider feed‐forward neural nets and their relation to regression models for survival data. We show how the back‐propagation algorithm may be used to obtain maximum likelihood estimates in certain standard regression models for survival data, as well as in various generalizations of these.
Liestøl, K. +2 more
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A neural net model for epilepsy
Journal of Theoretical Biology, 1977Abstract A neural net model based in our previous studies with randomly interconnected neural nets is presented here capable of exhibiting epileptic features. These features can be explained in terms of the structural and dynamical properties of the model. In addition, apart from the fact that this model can imitate epileptic phenomena, it might also
P A, Anninos, R, Cyrulnik
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Applications of Hybrid Fuzzy Neural Nets and Fuzzy Neural Nets
1998The two topics of this chapter are: build hybrid fuzzy neural nets to equal fuzzy expert systems, fuzzy input-output controllers, and to evaluate certain fuzzy functions; and (2) show how first training a fuzzy neural net can solve the overfitting problem mentioned in Chapter 3.
James J. Buckley, Thomas Feuring
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Neural nets and the puzzle of intentionality
2002In this work, we ask epistemological questions involved in making the intentional behavior the object of physical and mathematical inquiry. We show that the subjective component of intentionality can never become object of scientific inquiry, as related to self-consciousness.
Gianfranco Basti, Antonio L. Perrone
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International Journal of Neural Systems, 1992
Neural network learning techniques for the recognition of decays of charged tracks are improved by adding the track momentum to the input. This allows the use of one single network for a wide range of energies. The efficiency of this method is compared with previous results and conventional methods and the behaviour of the nets is discussed in detail.
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Neural network learning techniques for the recognition of decays of charged tracks are improved by adding the track momentum to the input. This allows the use of one single network for a wide range of energies. The efficiency of this method is compared with previous results and conventional methods and the behaviour of the nets is discussed in detail.
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Single-Layer Neural Net Competes with Multi-layer Neural Net
2008This paper presents a novel neural network with only one layer which can compete with multi-layer neural nets. This novel neural net is called a double-threshold single-layer neural net. The theoretical analysis and experiments show that it can demonstrate similar performance as multi-layer neural nets.
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Fuzzy representations in neural nets
1994Clear, crisp, precise and unambiguous: that is how you like your concepts, if you are a serial computer. But human concepts are in general vague, fuzzy or subject to borderline cases. Anyone who deals with information via computers knows the problems arising from having to categorise objects to fit the computer's crude pigeonholes, and how inflexible ...
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