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020 _a9783540445654
_9978-3-540-44565-4
024 7 _a10.1007/3-540-44565-X
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aSequence Learning
_h[electronic resource] :
_bParadigms, Algorithms, and Applications /
_cedited by Ron Sun, C.Lee Giles.
250 _a1st ed. 2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2001.
300 _aXII, 396 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1828
505 0 _ato 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 _aSequential 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 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 1 4 _aArtificial Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aAlgorithms.
700 1 _aSun, Ron.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGiles, C.Lee.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540415978
776 0 8 _iPrinted edition:
_z9783662186145
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1828
856 4 0 _uhttps://doi.org/10.1007/3-540-44565-X
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912 _aZDB-2-SXCS
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