EAN: 9789811979071

SPRINGERBRIEFS IN COMPUTER SCIENCE REPRESENTA...

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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