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 / edited by C.Lee Giles, Marco Gori.

Format:

Book

Contributor:

Giles, C. Lee, editor.

Gori, Marco, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1387.

Lecture Notes in Artificial Intelligence ; 1387

Language:

English

Subjects (All):

Computer architecture.

Computer programming.

Artificial intelligence.

Computers.

Microprocessors.

Data structures (Computer science).

Computer System Implementation.

Programming Techniques.

Artificial Intelligence.

Computation by Abstract Devices.

Processor Architectures.

Data Structures.

Local Subjects:

Computer System Implementation.

Programming Techniques.

Artificial Intelligence.

Computation by Abstract Devices.

Processor Architectures.

Data Structures.

Physical Description:

1 online resource (XIV, 438 pages).

Edition:

First edition 1998.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1998.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-69752-7

9783540697527

Access Restriction:

Restricted for use by site license.