Shankru Guggari, Umadevi V, Vijayakumar Kadappa

Advances in Partitioning Techniques (eBook, PDF)

A Prospective towards Artificial Intelligence

• Format: PDF

Produktbeschreibung

The book discusses various partitioning strategies tailored for traditional machine learning algorithms. This book is a vital resource, illuminating the path towards unlocking the full potential of partitioning in shaping the future of AI technologies.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 134
• Erscheinungstermin: 2. Juni 2025
• Englisch
• ISBN-13: 9781040362402
• Artikelnr.: 73814080

Autorenporträt

Shankru Guggari is a machine learning specialist who primarily focuses on enhancing the performance of machine learning techniques. His research interests include pattern recognition, explainable AI, and machine learning. He has published his work in various international conferences and journals and has over four years of academic experience.

Umadevi V, PhD from IIT Madras, is a Professor of Computer Science at BMS College of Engineering, Bangalore and a Senior IEEE member. She has published extensively in reputed journals and conferences and received grants for research in medical thermography.

Vijaya Kumar Kadappa obtained his PhD in from the Central University of Hyderabad in 2010 and working as Professor at the Department of Computer Applications, BMS College of Engineering, Bangalore. He has 30+ research publications. Kadappa is a life member of IUPR-AI, ISTE, and CSI.

Inhaltsangabe

1. Introduction to partitioning techniques 2. Partitioning techniques for
deep learning techniques 3. Graph-based partitioning techniques 4.
Partitioning techniques for Big Data 5. Partitioning techniques for edge
computing