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008 170212s2015 gw | s |||| 0|eng d
020 _a9783319099033
_9978-3-319-09903-3
024 7 _a10.1007/978-3-319-09903-3
_2doi
035 _ato000557744
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aArtificial Neural Networks
_helectronic resource
_bMethods and Applications in Bio-/Neuroinformatics /
_cedited by Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aIX, 488 p. 168 illus., 70 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSpringer Series in Bio-/Neuroinformatics,
_x2193-9349 ;
_v4
505 0 _aNeural Networks Theory and Models -- New Machine Learning Algorithms for Neural Networks -- Pattern Recognition, Classification and other Neural Network Applications.
520 _aThe book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.  .
650 0 _aengineering.
_9224332
650 0 _aNeurosciences.
_9302217
650 0 _aBioinformatics.
_9303853
650 0 _aComputational Intelligence.
_9307538
650 0 _aControl Engineering.
_9304706
650 1 4 _aEngineering.
_9224332
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aComputational Biology/Bioinformatics.
_9306755
650 2 4 _acontrol.
_9348605
650 2 4 _aNeurosciences.
_9302217
700 1 _aKoprinkova-Hristova, Petia.
_eeditor.
_9414901
700 1 _aMladenov, Valeri.
_eeditor.
_9414900
700 1 _aKasabov, Nikola K.
_eeditor.
_9463333
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aSpringer Series in Bio-/Neuroinformatics,
_9451418
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-09903-3
912 _aZDB-2-ENG
999 _c412626