Sequence Learning (Record no. 188794)

MARC details
000 -LEADER
fixed length control field 04287nam a22005775i 4500
001 - CONTROL NUMBER
control field 978-3-540-44565-4
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423132530.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121227s2001 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783540445654
-- 978-3-540-44565-4
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/3-540-44565-X
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
245 10 - TITLE STATEMENT
Title Sequence Learning
Medium [electronic resource] :
Remainder of title Paradigms, Algorithms, and Applications /
Statement of responsibility, etc edited by Ron Sun, C.Lee Giles.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2001.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2001.
300 ## - PHYSICAL DESCRIPTION
Extent XII, 396 p.
Other physical details online resource.
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-- computer
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-- online resource
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490 1# - SERIES STATEMENT
Series statement Lecture Notes in Artificial Intelligence,
International Standard Serial Number 2945-9141 ;
Volume number/sequential designation 1828
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note to Sequence Learning -- to Sequence Learning -- Sequence Clustering and Learning with Markov Models -- Sequence Learning via Bayesian Clustering by Dynamics -- Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series -- Sequence Prediction and Recognition with Neural Networks -- Anticipation Model for Sequential Learning of Complex Sequences -- Bidirectional Dynamics for Protein Secondary Structure Prediction -- Time in Connectionist Models -- On the Need for a Neural Abstract Machine -- Sequence Discovery with Symbolic Methods -- Sequence Mining in Categorical Domains: Algorithms and Applications -- Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model -- Sequential Decision Making -- Sequential Decision Making Based on Direct Search -- Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning -- Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making -- Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning -- Biologically Inspired Sequence Learning Models -- Multiple Forward Model Architecture for Sequence Processing -- Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing -- Attentive Learning of Sequential Handwriting Movements: A Neural Network Model.
520 ## - SUMMARY, ETC.
Summary, etc Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algorithms.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Theory of Computation.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algorithms.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Sun, Ron.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Giles, C.Lee.
Relator term editor.
Relator code edt
-- http://id.loc.gov/vocabulary/relators/edt
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783540415978
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783662186145
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Artificial Intelligence,
-- 2945-9141 ;
Volume number/sequential designation 1828
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/3-540-44565-X">https://doi.org/10.1007/3-540-44565-X</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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