| 000 -LEADER |
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| fixed length control field |
06420nam a2200733 i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
7047350 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152916.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
150222s2015 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781627053662 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781627053655 |
| Qualifying information |
print |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00626ED1V01Y201501AIM030 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaBNVSL)swl00404708 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)903883121 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q325.5 |
| Item number |
.B455 2015 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bellet, Aurélien., |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Metric learning / |
| Statement of responsibility, etc. |
Aurélien Bellet, Amaury Habrard, Marc Sebban. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2015. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (xi, 139 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electronic |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on artificial intelligence and machine learning, |
| International Standard Serial Number |
1939-4616 ; |
| Volume/sequential designation |
# 30 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages 115-138). |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Introduction -- 1.1 Metric learning in a nutshell -- 1.2 Related topics -- 1.3 Prerequisites and notations -- 1.4 Outline -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Metrics -- 2.1 General definitions -- 2.2 Commonly used metrics -- 2.2.1 Metrics for numerical data -- 2.2.2 Metrics for structured data -- 2.3 Metrics in machine learning and data mining -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Properties of metric learning algorithms -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4. Linear metric learning -- 4.1 Mahalanobis distance learning -- 4.1.1 Early approaches -- 4.1.2 Regularized approaches -- 4.2 Linear similarity learninG -- 4.3 Large-scale metric learning -- 4.3.1 Large n: online, stochastic and distributed optimization -- 4.3.2 Large d: metric learning in high dimensions -- 4.3.3 Large n and large d -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Nonlinear and local metric learning -- 5.1 Nonlinear methods -- 5.1.1 Kernelization of linear methods -- 5.1.2 Learning nonlinear forms of metrics -- 5.2 Learning multiple local metrics -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Metric learning for special settings -- 6.1 Multi-task and transfer learning -- 6.2 Learning to rank -- 6.3 Semi-supervised learning -- 6.3.1 Classic setting -- 6.3.2 Domain adaptation -- 6.4 Histogram data -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. Metric learning for structured data -- 7.1 String edit distance learning -- 7.1.1 Probabilistic methods -- 7.1.2 Gradient descent methods -- 7.2 Tree and graph edit distance learning -- 7.3 Metric learning for time series -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Generalization guarantees for metric learning -- 8.1 Overview of existing work -- 8.2 Consistency bounds for metric learning -- 8.2.1 Definitions -- 8.2.2 Bounds based on uniform stability -- 8.2.3 Bounds based on algorithmic robustness -- 8.3 Guarantees on classification performance -- 8.3.1 Good similarity learning for linear classification -- 8.3.2 Bounds based on Rademacher complexity -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
9. Applications -- 9.1 Computer vision -- 9.2 Bioinformatics -- 9.3 Information retrieval -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
10. Conclusion -- 10.1 Summary -- 10.2 Outlook -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
A. Proofs of chapter 8 -- Uniform stability -- Algorithmic robustness -- Similarity-based linear classifiers -- Bibliography -- Authors' biographies. |
| 506 1# - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Similarity 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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on February 22, 2015). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning. |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
metric learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
similarity learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
Mahalanobis distance |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
edit distance |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
structured data |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
learning theory |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Habrard, Amaury., |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Sebban, Marc., |
| Relator term |
author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781627053655 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on artificial intelligence and machine learning ; |
| Volume/sequential designation |
# 30. |
| International Standard Serial Number |
1939-4616 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
http://ieeexplore.ieee.org/servlet/opac?bknumber=7047350 |
