Artificial Neural Networks and Machine Learning – ICANN by Shinya Suzumura, Ryohei Nakano (auth.), Alessandro E. Villa, PDF

By Shinya Suzumura, Ryohei Nakano (auth.), Alessandro E. Villa, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm (eds.)

ISBN-10: 364233265X

ISBN-13: 9783642332654

ISBN-10: 3642332668

ISBN-13: 9783642332661

The two-volume set LNCS 7552 + 7553 constitutes the complaints of the twenty second foreign convention on man made Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers integrated within the lawsuits have been rigorously reviewed and chosen from 247 submissions. they're geared up in topical sections named: theoretical neural computation; info and optimization; from neurons to neuromorphism; spiking dynamics; from unmarried neurons to networks; advanced firing styles; move and movement; from sensation to notion; item and face acceptance; reinforcement studying; bayesian and echo country 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 e-book online Artificial Neural Networks and Machine Learning – ICANN PDF

The two-volume set LNCS 7552 + 7553 constitutes the lawsuits of the twenty second overseas convention on synthetic Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers incorporated within the complaints have been conscientiously reviewed and chosen from 247 submissions. they're equipped in topical sections named: theoretical neural computation; info 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 acceptance; reinforcement studying; bayesian and echo country 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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Additional resources for Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II

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Addison-Wesley (1984) 7. : The complex backpropagation algorithm. IEEE Trans. Signal Processing 39(9), 2101–2104 (1991) 8. : Learning method utilizing singular region of multilayer perceptron. In: Proc. 3rd Int. Conf. on Neural Computation Theory and Applications, pp. 106–111 (2011) 9. : Reducibility of the complex-valued neural network. Neural Information Processing - Letters and Reviews 2(3), 53–56 (2004) 10. : Uniqueness of the weights for minimal feedforward nets with a given input-output map.

Springer, Heidelberg (1999) 24. : Uniqueness of functional representations by Gaussian basis function networks. In: Proceedings of ICANN 1994, pp. 471–474. pt Abstract. Label Ranking problems are receiving increasing attention in machine learning. , the ranking of a set of financial analysts in terms of the quality of their recommendations). In this paper, we adapt a multilayer perceptron algorithm for label ranking. We focus on the adaptation of the Back-Propagation (BP) mechanism. Six approaches are proposed to estimate the error signal that is propagated by BP.

K}. 1 Label Ranking Given T = { xn , πn }, the goal in LR is to learn a function f : X → ΩL that mint imizes a given loss function function l = 1t n=1 τ (πn , πˆn ). With this mapping, we are able to predict a ranking πˆn of the labels in L for a new example xn . Loss functions in LR are typically based on measures of rank correlation, that assess the similarity between two rankings. One such measure is Kendall’s τ coefficient, denoted τ (πˆn , πn ). The LR error is defined as eτ (n) on the nth training example by eτ (n) = 1/2 − 1/2 · τ (πˆn , πn ).

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Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II by Shinya Suzumura, Ryohei Nakano (auth.), Alessandro E. Villa, Włodzisław Duch, Péter Érdi, Francesco Masulli, Günther Palm (eds.)


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