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Representation in Machine Learning / by M. N. Murty, M. Avinash.

By:

Murty, M. N

Contributor(s):

Material type: Text

TextSeries: SpringerBriefs in Computer SciencePublication details: USA Springer c 2023Edition: 1st ed. 2023Description: IX, 93 p. 24cmISBN:

97809811979071

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

006.31 23 MUR-R 2023 790174

Online resources:

Full text available from Springer Computer Science eBooks 2023 English/International

Contents:

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

In: Springer Nature eBookSummary: 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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Holdings ( 3 )
Title notes ( 3 )

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Barcode
Reference	Faculty of CS & IT Library Book Cart	Book	006.31 MUR-R 2023 790174 (Browse shelf(Opens below))	3	Not For Loan (Restricted Access)	790174
Books	Faculty of CS & IT Library Book Cart	Book	006.31 MUR-R 2023 790175 (Browse shelf(Opens below))	4	Available	790175
Books	Faculty of CS & IT Library Book Cart	Book	006.31 MUR-R 2023 790176 (Browse shelf(Opens below))	5	Available	790176

Total holds: 0

Include Index

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.