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001			278294
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008			150903s2005 xxu| o |||| 0|eng d
020			_a9780387250618 _9978-0-387-25061-8
024	7		_a10.1007/b106715 _2doi
035			_avtls000330096
039		9	_a201509030444 _bVLOAD _c201405070458 _dVLOAD _c201401311329 _dstaff _c201401311153 _dstaff _y201401291447 _zstaff _wmsplit0.mrc _x516
050		4	_aQ334-342
100	1		_aVovk, Vladimir. _eautor _9299490
245	1	0	_aAlgorithmic Learning in a Random World / _cby Vladimir Vovk, Alexander Gammerman, Glenn Shafer.
264		1	_aBoston, MA : _bSpringer US, _c2005.
300			_aXVI, 324 páginas, _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aConformal prediction -- Classification with conformal predictors -- Modifications of conformal predictors -- Probabilistic prediction I: impossibility results -- Probabilistic prediction II: Venn predictors -- Beyond exchangeability -- On-line compression modeling I: conformal prediction -- On-line compression modeling II: Venn prediction -- Perspectives and contrasts.
520			_aConformal prediction is a valuable new method of machine learning. Conformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accuracy and reliability. This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrapáginas, Topics and Features: * Describes how conformal predictors yield accurate and reliable predictions, complemented with quantitative measures of their accuracy and reliability * Handles both classification and regression problems * Explains how to apply the new algorithms to real-world data sets * Demonstrates the infeasibility of some standard prediction tasks * Explains connections with Kolmogorov’s algorithmic randomness, recent work in machine learning, and older work in statistics * Develops new methods of probability forecasting and shows how to use them for prediction in causal networks Researchers in computer science, statistics, and artificial intelligence will find the book an authoritative and rigorous treatment of some of the most promising new developments in machine learning. Practitioners and students in all areas of research that use quantitative prediction or machine learning will learn about important new methods.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aGammerman, Alexander. _eautor _9301823
700	1		_aShafer, Glenn. _eautor _9301824
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387001524
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/b106715 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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