SpringerBriefs in Computer Science Representation in Machine Learning - M N Murty M Avinash Kartoniert (TB)

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 Sensit ive 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.