Representation in Machine Learning / SpringerBriefs in Computer Science (PDF)
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
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.
Avinash M is a graduate student at the Indian Institute of Technology, Madras, India, whose main research interests include Signal Processing and ML. He was in the top 90 among the approximately 200,000 candidates taking the GATE (Graduate Aptitude Test in Engineering) examination in 2016 in India.
- Autoren: M. N. Murty , M. Avinash
- 2023, 1st ed. 2023, 93 Seiten, Englisch
- Verlag: Springer Nature Singapore
- ISBN-10: 9811979081
- ISBN-13: 9789811979088
- Erscheinungsdatum: 20.01.2023
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 4.01 MB
- Ohne Kopierschutz
- Vorlesefunktion
Schreiben Sie einen Kommentar zu "Representation in Machine Learning / SpringerBriefs in Computer Science".
Kommentar verfassen