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008			230914s2023 si | o |||| 0|eng d
020			_a9789819917907
024	7		_a10.1007/978-981-99-1790-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D35 _b2023 EB
100	1		_aYamanishi, Kenji _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689668
245	1	0	_aLearning with the Minimum Description Length Principle _cby Kenji Yamanishi
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
505	0		_aInformation and Coding -- Parameter Estimation -- Model Selection -- Latent Variable Model Selection -- Sequential Prediction -- MDL Change Detection -- Continuous Model Selection -- Extension of Stochastic Complexity -- Mathematical Preliminaries.
520			_aThis book introduces readers to the minimum description length (MDL) principle and its applications in learning. The MDL is a fundamental principle for inductive inference, which is used in many applications including statistical modeling, pattern recognition and machine learning. At its core, the MDL is based on the premise that "the shortest code length leads to the best strategy for learning anything from data." The MDL provides a broad and unifying view of statistical inferences such as estimation, prediction and testing and, of course, machine learning. The content covers the theoretical foundations of the MDL and broad practical areas such as detecting changes and anomalies, problems involving latent variable models, and high dimensional statistical inference, among others. The book offers an easy-to-follow guide to the MDL principle, together with other information criteria, explaining the differences between their standpoints. Written in a systematic, concise and comprehensive style, this book is suitable for researchers and graduate students of machine learning, statistics, information theory and computer science.
988			_aSpringer_Computer_2023
650		7	_2embne _9151535 _aEstructuras de datos (Informática)
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-1790-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI