Get Advances in Neural Networks: Computational and Theoretical PDF

By Simone Bassis, Anna Esposito, Francesco Carlo Morabito

ISBN-10: 3319181637

ISBN-13: 9783319181639

ISBN-10: 3319181645

ISBN-13: 9783319181646

This booklet collects study works that take advantage of neural networks and computer studying strategies from a multidisciplinary point of view. matters lined contain theoretical, methodological and computational issues that are grouped jointly into chapters dedicated to the dialogue of novelties and ideas on the topic of the sector of synthetic Neural Networks in addition to using neural networks for purposes, development acceptance, sign processing, and specified themes resembling the detection and popularity of multimodal emotional expressions and day-by-day cognitive features, and bio-inspired memristor-based networks.

Providing insights into the most recent learn curiosity from a pool of overseas specialists coming from varied study fields, the quantity turns into priceless to all people with any curiosity in a holistic method of enforce plausible, self sustaining, adaptive and context-aware details conversation Technologies.

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Extra info for Advances in Neural Networks: Computational and Theoretical Issues

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5), will be denoted by a small value of Eq. (8). We can use this quantity to prune neurons in a similar way with respect to the last section. In particular, every Q time instants we define the probability of removing a given node as: pi (n) = exp − |si (n)| tˆ(n) (9) The new temperature tˆ(n) must respect the same properties depicted in the last section. Practically, in all our experiments we use the exponential profile defined by Eq. (7). The overall algorithm, inclusive of pruning of the neurons and of the synapses, is summarized in Algorithm 1.

Inform. Sciences 264, 104–117 (2014) 2. : Functional link based architectures for nonlinear acoustic echo cancellation. In: Proc. IEEE J. Works. Hands-free Speech Commun. Microph. Arrays (HSCMA 2011), Edinburgh, UK, pp. 180–184 (May 2011) 3. : Functional link adaptive filters for nonlinear acoustic echo cancellation. IEEE Trans. Audio, Speech, Lang. Process. 21(7), 1502–1512 (2013) 4. : Nonlinear acoustic echo cancellation based on sparse functional link representations. To appear in IEEE Trans.

References 1. : Fast decorrelated neural network ensambles with random weights. Inform. Sciences 264, 104–117 (2014) 2. : Functional link based architectures for nonlinear acoustic echo cancellation. In: Proc. IEEE J. Works. Hands-free Speech Commun. Microph. Arrays (HSCMA 2011), Edinburgh, UK, pp. 180–184 (May 2011) 3. : Functional link adaptive filters for nonlinear acoustic echo cancellation. IEEE Trans. Audio, Speech, Lang. Process. 21(7), 1502–1512 (2013) 4. : Nonlinear acoustic echo cancellation based on sparse functional link representations.

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Advances in Neural Networks: Computational and Theoretical Issues by Simone Bassis, Anna Esposito, Francesco Carlo Morabito


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