Artificial Neural Networks

Methods and Applications in Bio-/Neuroinformatics

Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov (Herausgeber)

Buch | Softcover

IX, 488 Seiten

2016 | Softcover reprint of the original 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-34950-3 (ISBN)

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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.

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

Erscheinungsdatum	05.03.2022
Reihe/Serie	Springer Series in Bio-/Neuroinformatics
Zusatzinfo	IX, 488 p. 168 illus., 98 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	749 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	ANN in bioinformatics • ANN in neuroinformatics • Artificial Intelligence • automatic control engineering • Computational Biology/Bioinformatics • Computational Intelligence • Control • Dynamic Neural Networks • Embodied Models • Engineering • Engineering: general • gesture recognition • ICANN 2013 • Information technology: general issues • Life sciences: general issues • Neurosciences • Recurrent Neural Networks • SVMs • Time-dependent learning • time series data
ISBN-10	3-319-34950-3 / 3319349503
ISBN-13	978-3-319-34950-3 / 9783319349503
Zustand	Neuware