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008			230211s2009 sz | s |||| 0|eng d
020			_a9783031015489
024	7		_a10.1007/978-3-031-01548-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.75 _b2009 EB
100	1		_aZhu, Xiaojin, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686715 _cPh. D.
245	1	0	_aIntroduction to Semi-Supervised Learning _cby Xiaojin Zhu, Andrew. B Goldberg
250			_a1st edition 2009
264		1	_aCham _bSpringer International Publishing _c2009
300			_a1 recurso en línea (XII, 116 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aIntroduction to Statistical Machine Learning -- Overview of Semi-Supervised Learning -- Mixture Models and EM -- Co-Training -- Graph-Based Semi-Supervised Learning -- Semi-Supervised Support Vector Machines -- Human Semi-Supervised Learning -- Theory and Outlook.
520			_aSemi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook.
988			_aSynthesis Collection of Technology_2009
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aGoldberg, A. B. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686716 _q(Andrew B.)
776	0	8	_iPrinted edition: _z9783031004209
776	0	8	_iPrinted edition: _z9783031026768
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01548-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI