TY  - BOOK
AU  - Murty,M.N.
AU  - Avinash,M.
TI  - Representation in Machine Learning
T2  - SpringerBriefs in Computer Science,
SN  - 9789811979088
U1  - 006.31 23
PY  - 2023///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Machine learning
KW  - Artificial intelligence
KW  - Data processing
KW  - Machine Learning
KW  - Data Science
KW  - Artificial Intelligence
N1  - 1. Introduction -- 2. Representation -- 3. Nearest Neighbor Algorithms -- 4. Representation Using Linear Combinations -- 5. Non-Linear Schemes for Representation -- 6. Conclusions
N2  - 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
UR  - https://doi.org/10.1007/978-981-19-7908-8
ER  -