Download Artificial Neural Networks: Methods and Applications in by Valeri Mladenov, Petia Koprinkova-Hristova PDF

By Valeri Mladenov, Petia Koprinkova-Hristova

The publication stories at the most modern theories on man made neural networks, with a unique emphasis on bio-neuroinformatics tools.

It comprises twenty-three papers chosen from probably the greatest contributions on bio-neuroinformatics-related matters, which have been awarded on the overseas convention on synthetic Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013

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Extra resources for Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics

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1178–1183 (2000) 2. : Efficient simulation of finite automata by neural nets. J. ACM 38(2), 495–514 (1991) 3. : An algebraic framework to represent finite state machines in single-layer recurrent neural networks. Neural Computation 7(5), 931–949 (1995) 4. : On Modelling the Nervous System. L. ) Principles and Practice of Bionics, Proc. 44th. AGARD— Conference Br¨ussel, ch. 1-2, pp. 43–58. The Advisory Group for Aerospace Research and Development, NATO (1970) 5. : Computational power of neural networks: a characterization in terms of kolmogorov complexity.

Cichocki Remark 1. Assuming (XX T )−1 is calculated with the O(J 3 ) complexity, then one iterative step of the Newton method for updating the matrix A entails the complexity about O(IJT ) + O(J 3 + T J 2 ) + O(IJ 2 ). Assuming J << min{I, T }, an overall complexity is dominated by O(IJT ). Remark 2. The Hessian H X is no longer a block-diagonal matrix, hence the Newton updates for X cannot be simplified considerably. The computational complexity of one step of the Newton algorithm is O(IJT ) + O(J 3 T 3 ) + O(JT ).

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation. Wiley and Sons (2009) 7. : Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Transaction on Signal Processing 53(7), 2477–2488 (2005) 8. : When does non-negative matrix factorization give a correct decomposition into parts? , Sch¨olkopf, B. ) Advances in Neural Information Processing Systems (NIPS), vol. 16. MIT Press, Cambridge (2004) 9.

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