Representation in Machine Learning (SpringerBriefs in Computer Science) - Tapa blanda

Libro 24 de 101: SpringerBriefs in Computer Science

Murty, M. N.; Avinash, M.

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

Sinopsis

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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Acerca del 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.

De la contraportada

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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Editorial
Springer
Año de publicación
2023
Idioma
Inglés
ISBN 10 
9811979073
ISBN 13 
9789811979071
Encuadernación
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Número de edición
1
Número de páginas
104
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Alemania

Otras ediciones populares con el mismo título

9789811979095: Representation in Machine Learning

Edición Destacada

ISBN 10:  981197909X ISBN 13:  9789811979095
Editorial: Springer, 2023
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