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  • Produktbild: Adaptive Processing of Sequences and Data Structures
  • Produktbild: Adaptive Processing of Sequences and Data Structures
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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

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Beschreibung

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...

Beschreibung

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

Einband

Taschenbuch

Erscheinungsdatum

25.03.1998

Abbildungen

XIV, 438 p.

Herausgeber C.Lee Giles + weitere

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.03.1998

Abbildungen

XIV, 438 p.

Herausgeber
Verlag Springer Berlin
Seitenzahl

438

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

680 g

Auflage

1998

Sprache

Englisch

ISBN

978-3-540-64341-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Adaptive Processing of Sequences and Data Structures
  • Produktbild: Adaptive Processing of Sequences and Data Structures
  • 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.