Representation in Machine Learning / by M. N. Murty, M. Avinash.
Material type: TextSeries: SpringerBriefs in Computer SciencePublication details: USA Springer c 2023Edition: 1st ed. 2023Description: IX, 93 p. 24cmISBN:- 97809811979071
- 006.31 23 MUR-R 2023 790174
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006.31 MIR-M 2022 790204 Machine learning : theory to applications / | 006.31 MIR-M 2022 790205 Machine learning : theory to applications / | 006.31 MIR-M 2022 790206 Machine learning : theory to applications / | 006.31 MUR-R 2023 790174 Representation in Machine Learning / | 006.31 MUR-R 2023 790175 Representation in Machine Learning / | 006.31 MUR-R 2023 790176 Representation in Machine Learning / | 006.31 PAJ-H 2022 790138 HANDS-ON MACHINE LEARNING WITH PYTHON implement neural network solutions with scikit-learn and... pytorch |
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.