000 | 04287nam a22005775i 4500 | ||
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001 | 978-3-540-44565-4 | ||
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005 | 20240423132530.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2001 gw | s |||| 0|eng d | ||
020 |
_a9783540445654 _9978-3-540-44565-4 |
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024 | 7 |
_a10.1007/3-540-44565-X _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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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. |
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300 |
_aXII, 396 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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 |
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700 | 1 |
_aGiles, C.Lee. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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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 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
912 | _aZDB-2-LNC | ||
912 | _aZDB-2-BAE | ||
942 | _cSPRINGER | ||
999 |
_c188794 _d188794 |