Giles

Adaptive Processing of Sequences and Data Structures

International Summer School on Neural Networks, "E.R. Caianiello", Vietri sul Mare, Salerno, Italy, September 6-13, 1997, Tutorial Lectures
Herausgegeben:Giles, C.Lee; Gori, Marco

• Broschiertes Buch

Produktbeschreibung

The 15 coherent tutorial chapters contributed by leading authorities from the field of neural networks are organized in parts on architectures and learning in recurrent neural networks, processing of data structures, probabilistic models, analog versus symbolic computation, and applications (in pattern recognition and speech processing).

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This book is devoted to adaptive processing of structured information similar to flexible and intelligent information processing by humans - in contrast to merely sequential processing of predominantly symbolic information within a deterministic framework. Adaptive information processing allows for a mixture of sequential and parallel processing of symbolic as well as subsymbolic information within deterministic and probabilistic frameworks.
The book originates from a summer school held in September 1997 and thus is ideally suited for advanced courses on adaptive information processing and advanced learning techniques or for self-instruction. Research and design professionals active in the area of neural information processing will find it a valuable state-of-the-art survey.

Produktdetails

• Lecture Notes in Computer Science 1387
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 10632079, 978-3-540-64341-8
• 1998
• Seitenzahl: 452
• Erscheinungstermin: 25. März 1998
• Englisch
• Abmessung: 235mm x 155mm x 25mm
• Gewicht: 597g
• ISBN-13: 9783540643418
• ISBN-10: 3540643419
• Artikelnr.: 09228475

Inhaltsangabe

Recurrent neural network architectures: An overview.- Gradient based learning methods.- Diagrammatic methods for deriving and relating temporal neural network algorithms.- An introduction to learning structured information.- Neural networks for processing data structures.- The loading problem: Topics in complexity.- Learning dynamic Bayesian networks.- Probabilistic models of neuronal spike trains.- Temporal models in blind source separation.- Recursive neural networks and automata.- The neural network pushdown automaton: Architecture, dynamics and training.- Neural dynamics with stochasticity.- Parsing the stream of time: The value of event-based segmentation in a complex real-world control problem.- Hybrid HMM/ANN systems for speech recognition: Overview and new research directions.- Predictive models for sequence modelling, application to speech and character recognition.