Download Artificial Neural Networks and Machine Learning – ICANN by Claire Gerrard, John McCall, George M. Coghill, Christopher PDF

By Claire Gerrard, John McCall, George M. Coghill, Christopher Macleod (auth.), Alessandro E. P. Villa, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm (eds.)

The two-volume set LNCS 7552 + 7553 constitutes the court cases of the twenty second overseas convention on man made Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers integrated within the lawsuits have been conscientiously reviewed and chosen from 247 submissions. they're geared up in topical sections named: theoretical neural computation; details and optimization; from neurons to neuromorphism; spiking dynamics; from unmarried neurons to networks; advanced firing styles; move and movement; from sensation to conception; item and face reputation; reinforcement studying; bayesian and echo nation networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the mind; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; education and studying; inference and popularity; aid vector machines; self-organizing maps and clustering; clustering, mining and exploratory research; bioinformatics; and time weries and forecasting.

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Read or Download Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part I PDF

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Litinskii The catastrophic forgetting is a troublesome defect of the Hopfield model. Indeed, let us imagine a robot whose memory is based on the Hopfield model. It is natural to think that his memory is steadily filled up. Catastrophic forgetting means that when the number of stored patterns exceeds M c , the memory will be completely destructed. Everything that was accumulated in the memory would be forgotten. Earlier some modifications of the Hebb matrix were proposed to eliminate the memory destruction [4]-[7].

The standard Hopfield model is generalized to the case when input patterns are provided with weights that are proportional to the frequencies of patterns occurrence at the learning process. The main equation is derived by methods of statistical physics, and is solved for an arbitrary distribution of weights. An infinitely large number of input patterns can be written down in connection matrix however the memory of the network will consist of patterns whose weights exceed a critical value. The approach eliminates the catastrophic destruction of the memory characteristic to the standard Hopfield model.

However, all these models have the same weak point: only patterns that are the last written down in the connection matrix constitute the memory of the network. Patterns, that were previously written down in the connection are excluded from the memory irretrievably. We succeeded in eliminating of the catastrophic forgetting of the Hopfield model. In our approach every pattern is supplied by an individual weight. In place of the Hebbian matrix we obtain a quasi-Hebbian connection of the form M J ij ~ (1 − δ ij ) rμ xiμ x μj .

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