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008			220601s2014 sz | s |||| 0|eng d
020			_a9783031015717
024	7		_a10.1007/978-3-031-01571-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.75 _b2014 EB
100	1		_aSubramanya, Amarnag _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686626
245	1	0	_aGraph-Based Semi-Supervised Learning _cby Amarnag Subramanya, Partha Pratim Talukdar
250			_a1st edition 2014
264		1	_aCham _bSpringer International Publishing _c2014
300			_a1 recurso en línea (XIII, 111 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 -- Graph Construction -- Learning and Inference -- Scalability -- Applications -- Future Work -- Bibliography -- Authors' Biographies -- Index.
520			_aWhile labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index.
988			_aSynthesis Collection of Technology_2014
650		7	_2embne _9166090 _aAprendizaje automático _xMétodos gráficos
700	1		_aTalukdar, Partha Pratim _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686627
776	0	8	_iPrinted edition: _z9783031004438
776	0	8	_iPrinted edition: _z9783031026997
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01571-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI