Representation in Machine Learning (Paperback, 1st ed. 2023)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs in Computer Science
Release date:	2023
First published:	2023
Authors:	M.N. Murty • M Avinash
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	93
Edition:	1st ed. 2023
ISBN-13:	978-981-19-7907-1
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	981-19-7907-3
Barcode:	9789811979071

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