000			03358nam a22005055i 4500
001			978-0-387-69082-7
003			DE-He213
005			20170628033331.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387690827 _9978-0-387-69082-7
024	7		_a10.1007/978-0-387-69082-7 _2doi
050		4	_aQA76.9.M35
072		7	_aPBD _2bicssc
072		7	_aUYAM _2bicssc
072		7	_aCOM018000 _2bisacsh
072		7	_aMAT008000 _2bisacsh
082	0	4	_a004.0151 _223
100	1		_aCao, Xi-Ren. _eauthor.
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.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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	_aComputational complexity.
650		0	_aArtificial intelligence.
650		0	_aDistribution (Probability theory).
650	1	4	_aComputer Science.
650	2	4	_aDiscrete Mathematics in Computer Science.
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.
650	2	4	_aOperations Research/Decision Theory.
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
999			_c14716 _d14716