大葉大學圖書館 |

Artificial neural networks[electronic resource] :methods and applications in bio-/neuroinformatics /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3
書名/作者:	Artificial neural networks : methods and applications in bio-/neuroinformatics // edited by Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov.
其他作者:	Koprinkova-Hristova, Petia.
團體作者:	Clark Conference
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	x, 488 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science)
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Computational Biology/Bioinformatics.
標題:	Control.
標題:	Neurosciences.
ISBN:	9783319099033 (electronic bk.)
ISBN:	9783319099026 (paper)
內容註:	Neural Networks Theory and Models -- New Machine Learning Algorithms for Neural Networks -- Pattern Recognition, Classification and other Neural Network Applications.
摘要、提要註:	The 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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-09903-3

Artificial neural networks[electronic resource] :methods and applications in bio-/neuroinformatics /
Artificial neural networksmethods and applications in bio-/neuroinformatics /[electronic resource] :edited by Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov. - Cham :Springer International Publishing :2015. - x, 488 p. :ill. (some col.), digital ;24 cm. - Springer series in bio-/neuroinformatics,v.42193-9349 ;. - Springer series in bio-/neuroinformatics ;v.3..

Neural Networks Theory and Models -- New Machine Learning Algorithms for Neural Networks -- Pattern Recognition, Classification and other Neural Network Applications.

The 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.

ISBN: 9783319099033 (electronic bk.)

Standard No.: 10.1007/978-3-319-09903-3doiSubjects--Topical Terms:

386157
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.3

Artificial neural networks[electronic resource] :methods and applications in bio-/neuroinformatics /
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