By Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov
The publication experiences at the most modern theories on man made neural networks, with a unique emphasis on bio-neuroinformatics equipment. It comprises twenty-three papers chosen from top-of-the-line contributions on bio-neuroinformatics-related matters, which have been provided on the foreign convention on man made Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The publication covers a vast variety of themes in regards to the idea and functions of synthetic neural networks, together with recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired versions of telephone groups, Gestalt legislation, embodied idea of language realizing, 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 reviews on new methods to reinforcement studying, optimum keep an eye on of discrete time-delay structures, new algorithms for prototype choice, and workforce constitution gaining knowledge of. additionally, the booklet discusses one-class aid vector machines for trend acceptance, handwritten digit acceptance, time sequence forecasting and category, and anomaly id in facts analytics and automatic facts research. by means of proposing the state of the art and discussing the present demanding situations within the fields of synthetic neural networks, bioinformatics and neuroinformatics, the e-book is meant to advertise the implementation of latest equipment and development of latest ones, and to help complicated scholars, researchers and pros of their day-by-day efforts to spot, comprehend and clear up a few open questions in those fields.
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Extra resources for Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics
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Dataset B: It is created from images of hand-written digits from 0 to 9. They were prepared by one student from Wroclaw University of Technology, and used in . The images are downsampled to the resolution of 64 × 64 pixels. Each class contains 10 images. For testing the algorithms with this dataset, we used the regular 5-fold CV rule. edu Image Classification with NMF Based on SPG 43 Table 1 Mean recognition rates, standard deviations (in parenthesis), and elapsed time averaged over CV-folds for J = 30, and the datasets: A, B1 (dataset B without processing), B2 (dataset B with WT processing) and C.
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Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics by Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov