Representation in Machine Learning
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. Les mer
Logg inn for å se din bonus
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.
Detaljer
- Forlag
- Springer Nature Singapore
- Språk
- Engelsk
- Sider
- 0
- ISBN
- 9789811979088
- Utgivelsesår
- 2023
- Serie
-
SpringerBriefs in Computer Science
Medlemmers vurdering
Skriv en vurdering
Oppdag mer
Error 429 Too many requests
Too many requests
Guru Meditation:
XID: 30759994
Varnish cache server
Error 429 Too many requests
Too many requests
Guru Meditation:
XID: 30759995
Varnish cache server
Error 429 Too many requests
Too many requests
Guru Meditation:
XID: 30759996
Varnish cache server
Error 429 Too many requests
Too many requests
Guru Meditation:
XID: 30759997
Varnish cache server