Read e-book online Artificial Neural Networks: Methods and Applications in PDF

By Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov

ISBN-10: 3319099027

ISBN-13: 9783319099026

ISBN-10: 3319099035

ISBN-13: 9783319099033

The booklet experiences at the most up-to-date theories on synthetic neural networks, with a distinct emphasis on bio-neuroinformatics equipment. It contains twenty-three papers chosen from among the finest contributions on bio-neuroinformatics-related concerns, that have been offered on the foreign convention on synthetic Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The ebook covers a extensive variety of themes about the concept and purposes of synthetic neural networks, together with recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired types of cellphone groups, Gestalt legislation, embodied idea of language knowing, saccadic gaze shifts and reminiscence formation, and new education algorithms for Deep Boltzmann Machines, in addition to dynamic neural networks and kernel machines. It additionally studies on new techniques to reinforcement studying, optimum regulate of discrete time-delay platforms, new algorithms for prototype choice, and workforce constitution researching. in addition, the e-book discusses one-class help vector machines for trend popularity, handwritten digit acceptance, time sequence forecasting and class, and anomaly identity in information analytics and automatic info research. by means of featuring the state of the art and discussing the present demanding situations within the fields of synthetic neural networks, bioinformatics and neuroinformatics, the ebook is meant to advertise the implementation of latest tools and development of latest ones, and to help complex scholars, researchers and execs of their day-by-day efforts to spot, comprehend and remedy a couple of open questions in those fields.

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Neural Syst. 18(4), 267–277 (2008) Recurrent Neural Networks and Super-Turing Interactive Computation 27 34. : Exuberance in the development of cortical networks. Nature Rev. Neurosci. 6, 955–965 (2005) 35. : Chaotic itinerancy. Chaos 13(3), 926–936 (2003) 36. : Evolving connectionist systems - the knowledge engineering approach, 2nd edn. Springer (2007) 37. : The dynamic universality of sigmoidal neural networks. Inf. Comput. 128(1), 48–56 (1996) 38. : Representation of events in nerve nets and finite automata.

However, the computational problem can be considerably alleviated if the modified Spectral Projected Gradient (SPG) that belongs to a class of quasi-Newton methods is used. The simulation results presented for the selected classification problems demonstrate the high efficiency of the proposed method. 1 Introduction Nonnegative Matrix Factorization (NMF) [20] decomposes a nonnegative matrix into lower-rank factor matrices that have nonnegative entries and usually some physical meaning. When NMF is applied to the matrix of training samples, we obtain sparse nonnegative feature vectors and coefficients of their nonnegative combinations.

CAIP 2005. LNCS, vol. 3691, pp. 407–414. Springer, Heidelberg (2005) 10. : Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Transactions on Image Processing 20(7), 2030–2048 (2011) 11. : NeNMF: An optimal gradient method for nonnegative matrix factorization. IEEE Transactions on Signal Processing 60(6), 2882–2898 (2012) 12. : Classifying faces with nonnegative matrix factorization. In: Proc. 5th Catalan Conference for Artificial Intelligence, Castello de la Plana, Spain, pp.

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Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics by Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov

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