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Representation in Machine Learning - M. N. Murty, M. Avinash

Representation in Machine Learning (eBook)

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eBook Download: PDF
2023 | 1st ed. 2023
IX, 93 Seiten
Springer Nature Singapore (Verlag)
978-981-19-7908-8 (ISBN)
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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.




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.


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.
Erscheint lt. Verlag 20.1.2023
Reihe/Serie SpringerBriefs in Computer Science
SpringerBriefs in Computer Science
Zusatzinfo IX, 93 p. 1 illus.
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Theorie / Studium Algorithmen
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Schlagworte Artificial Intelligence • autoencoder • Data Mining • dimensionality reduction • Locality Sensitive Hashing • machine learning • principal component • Representation
ISBN-10 981-19-7908-1 / 9811979081
ISBN-13 978-981-19-7908-8 / 9789811979088
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