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008			220601s2015 sz | o |||| 0|eng d
020			_a9783031015724
024	7		_a10.1007/978-3-031-01572-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2015 EB
100	1		_aBellet, Aurélien _d1986- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686151
245	1	0	_aMetric Learning _cby Aurélien Bellet, Amaury Habrard, Marc Sebban
250			_a1st edition 2015
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XI, 139 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 -- Metrics -- Properties of Metric Learning Algorithms -- Linear Metric Learning -- Nonlinear and Local Metric Learning -- Metric Learning for Special Settings -- Metric Learning for Structured Data -- Generalization Guarantees for Metric Learning -- Applications -- Conclusion -- Bibliography -- Authors' Biographies .
520			_aSimilarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric Learning Algorithms / Linear Metric Learning / Nonlinear and Local Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' Biographies.
988			_aSynthesis Collection of Technology_2015
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aHabrard, Amaury _d1978- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686152
700	1		_aSebban, Marc _d1969- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686153
776	0	8	_iPrinted edition: _z9783031004445
776	0	8	_iPrinted edition: _z9783031027000
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01572-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI