000 04656nam a22005415i 4500
001 978-3-031-01572-4
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008 220601s2015 sz | s |||| 0|eng d
020 _a9783031015724
_9978-3-031-01572-4
024 7 _a10.1007/978-3-031-01572-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aBellet, Aurélien.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978970
245 1 0 _aMetric Learning
_h[electronic resource] /
_cby Aurélien Bellet, Amaury Habrard, Marc Sebban.
250 _a1st ed. 2015.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXI, 139 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_978971
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aHabrard, Amaury.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978972
700 1 _aSebban, Marc.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978973
710 2 _aSpringerLink (Online service)
_978974
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004445
776 0 8 _iPrinted edition:
_z9783031027000
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_978975
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01572-4
912 _aZDB-2-SXSC
942 _cEBK
999 _c84689
_d84689