000 | 03506nam a22005535i 4500 | ||
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001 | 978-0-387-69082-7 | ||
003 | DE-He213 | ||
005 | 20161121230609.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2007 xxu| s |||| 0|eng d | ||
020 |
_a9780387690827 _9978-0-387-69082-7 |
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024 | 7 |
_a10.1007/978-0-387-69082-7 _2doi |
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050 | 4 | _aQA76.9.M35 | |
072 | 7 |
_aPBD _2bicssc |
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072 | 7 |
_aUYAM _2bicssc |
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072 | 7 |
_aCOM018000 _2bisacsh |
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072 | 7 |
_aMAT008000 _2bisacsh |
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082 | 0 | 4 |
_a004.0151 _223 |
100 | 1 |
_aCao, Xi-Ren. _eauthor. |
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245 | 1 | 0 |
_aStochastic Learning and Optimization _h[electronic resource] : _bA Sensitivity-Based Approach / _cby Xi-Ren Cao. |
264 | 1 |
_aBoston, MA : _bSpringer US, _c2007. |
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300 |
_aXX, 566 p. 119 illus. With 212 Problems. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aFour Disciplines in Learning and Optimization -- Perturbation Analysis -- Learning and Optimization with Perturbation Analysis -- Markov Decision Processes -- Sample-Path-Based Policy Iteration -- Reinforcement Learning -- Adaptive Control Problems as MDPs -- The Event-Based Optimization - A New Approach -- Event-Based Optimization of Markov Systems -- Constructing Sensitivity Formulas. | |
520 | _aStochastic learning and optimization is a multidisciplinary subject that has wide applications in modern engineering, social, and financial problems, including those in Internet and wireless communications, manufacturing, robotics, logistics, biomedical systems, and investment science. This book is unique in the following aspects. (Four areas in one book) This book covers various disciplines in learning and optimization, including perturbation analysis (PA) of discrete-event dynamic systems, Markov decision processes (MDP)s), reinforcement learning (RL), and adaptive control, within a unified framework. (A simple approach to MDPs) This book introduces MDP theory through a simple approach based on performance difference formulas. This approach leads to results for the n-bias optimality with long-run average-cost criteria and Blackwell's optimality without discounting. (Event-based optimization) This book introduces the recently developed event-based optimization approach, which opens up a research direction in overcoming or alleviating the difficulties due to the curse of dimensionality issue by utilizing the system's special features. (Sample-path construction) This book emphasizes physical interpretations based on the sample-path construction. | ||
650 | 0 | _aComputer science. | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aCalculus of variations. | |
650 | 0 | _aProbabilities. | |
650 | 0 | _aEngineering design. | |
650 | 0 | _aControl engineering. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aDiscrete Mathematics in Computer Science. |
650 | 2 | 4 | _aEngineering Design. |
650 | 2 | 4 | _aControl. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aCalculus of Variations and Optimal Control; Optimization. |
650 | 2 | 4 | _aProbability Theory and Stochastic Processes. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9780387367873 |
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-0-387-69082-7 |
912 | _aZDB-2-SCS | ||
950 | _aComputer Science (Springer-11645) | ||
999 |
_c501139 _d501139 |