Representation in Machine Learning

Name: Representation in Machine Learning
SKU: 36029566
Price: 136.99 AUD
Availability: InStock

By:

Format:

Paperback / softback

Series:

Springerbriefs in Computer Science

Show Hide Adult Content

$136.99

Available from supplier

Usually ships in 3-4 weeks

0 in Trolley

Price Match

Buy Now, Pay Later with:

4 payments of $34.25 with Afterpay Learn more

Availability

Delivering to:

Estimated arrival:

Around 12-24 June using International Courier

Description

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.

Author Biography:

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.

Release date Australia

January 21st, 2023

Author

Series

Springerbriefs in Computer Science

Pages

Edition

1st ed. 2023

Audience

Professional & Vocational

Illustrations

1 Illustrations, black and white; IX, 93 p. 1 illus.

ISBN-13

9789811979071

Product ID

36029566

Customer reviews

Nobody has reviewed this product yet. You could be the first!

Write a Review

Marketplace listings

There are no Marketplace listings available for this product currently.
Already own it? Create a free listing and pay just 9% commission when it sells!

Sell Yours Here