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Representation in machine learning

Murty, M. N.

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Representation in machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Representation in machine learning/ by M. N. Murty, M. Avinash.
Author:	Murty, M. N.
other author:	Avinash, M.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	ix, 93 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Representation -- 3. Nearest Neighbor Algorithms -- 4. Representation Using Linear Combinations -- 5. Non-Linear Schemes for Representation -- 6. Conclusions.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-19-7908-8
ISBN:	9789811979088

Representation in machine learning
Murty, M. N.

Representation in machine learning[electronic resource] /by M. N. Murty, M. Avinash. - Singapore :Springer Nature Singapore :2023. - ix, 93 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

1. Introduction -- 2. Representation -- 3. Nearest Neighbor Algorithms -- 4. Representation Using Linear Combinations -- 5. Non-Linear Schemes for Representation -- 6. Conclusions.

This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML) State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.

ISBN: 9789811979088

Standard No.: 10.1007/978-981-19-7908-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Representation in machine learning
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245 1 0 $a Representation in machine learning $h [electronic resource] / $c by M. N. Murty, M. Avinash.
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505 0 $a 1. Introduction -- 2. Representation -- 3. Nearest Neighbor Algorithms -- 4. Representation Using Linear Combinations -- 5. Non-Linear Schemes for Representation -- 6. Conclusions.
520 $a This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML) State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book. In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques' effectiveness.
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