Metric Learning

Released on by Aurélien Muise
preview-18

  • Metric Learning Book Detail

  • Author : Aurélien Muise
  • Release Date : 2022-05-31
  • Publisher : Springer Nature
  • Genre : Computers
  • Pages : 139
  • ISBN 13 : 303101572X
  • File Size : 16,16 MB
DOWNLOAD BOOK

Metric Learning by Aurélien Muise PDF Summary

Book Description: 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. 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

Disclaimer: www.yourbookbest.com does not own Metric Learning books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Metric Learning

Metric Learning

File Size : 28,28 MB
Total View : 5532 Views
DOWNLOAD

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropria

Metric Learning

Metric Learning

File Size : 71,71 MB
Total View : 7065 Views
DOWNLOAD

Metric Learning: A Review presents an overview of existing research in metric learning, including recent progress on scaling to high-dimensional feature spaces

Metric Learning

Metric Learning

File Size : 76,76 MB
Total View : 3590 Views
DOWNLOAD

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropria

Computer Vision – ECCV 2012

Computer Vision – ECCV 2012

File Size : 16,16 MB
Total View : 1874 Views
DOWNLOAD

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held

Metric in Minutes

Metric in Minutes

File Size : 27,27 MB
Total View : 4177 Views
DOWNLOAD

Covers everything you need to know about the metric system (système internationale, SI), from its history to practical tips on conversions and problem solving.