000 02265nam a22003618i 4500
001 CR9780511546921
003 UkCbUP
005 20200124160304.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 090508s2006||||enk o ||1 0|eng|d
020 _a9780511546921 (ebook)
020 _z9780521841085 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aQA269
_b.C45 2006
082 0 0 _a519.3
_222
100 1 _aCesa-Bianchi, Nicolò,
_d1963-
_eauthor.
245 1 0 _aPrediction, learning, and games /
_cNicolo Cesa-Bianchi, Gabor Lugosi.
246 3 _aPrediction, Learning, & Games
264 1 _aCambridge :
_bCambridge University Press,
_c2006.
300 _a1 online resource (xii, 394 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aThis important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
650 0 _aGame theory.
650 0 _aMachine learning.
650 0 _aComputer algorithms.
700 1 _aLugosi, Gábor,
_eauthor.
776 0 8 _iPrint version:
_z9780521841085
856 4 0 _uhttps://doi.org/10.1017/CBO9780511546921
999 _c520599
_d520597