Representation in Machine Learning (SpringerBriefs in Computer Science) - Softcover

Buch 22 von 60: SpringerBriefs in Computer Science

Murty, M. N.; Avinash, M.

 
9789811979071: Representation in Machine Learning (SpringerBriefs in Computer Science)
Softcover
ISBN 10: 9811979073 ISBN 13: 9789811979071
Verlag: Springer, 2023

Inhaltsangabe

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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Über die Autorin bzw. den Autor

M Narasimha Murty is a prominent researcher in the areas of ML and AI. He has co-authored an introductory book on Pattern Recognition, published by Springer, that is widely used by teachers and researchers. He led the team that won the ACMKDD Cup in 2003. He has collaborated with and worked at several institutions in India, the USA and Europe.

Avinash M is a graduate student at the Indian Institute of Technology, Madras, India, whose main research interests include Signal Processing and ML. He was in the top 90 among the approximately 200,000 candidates taking the GATE (Graduate Aptitude Test in Engineering) examination in 2016 in India.

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
9811979073
ISBN 13 
9789811979071
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
104
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ISBN 10:  981197909X ISBN 13:  9789811979095
Verlag: Springer, 2023
Softcover