Handling Uncertainty in Artificial Intelligence - Brossura

Chaki, Jyotismita

Brossura

ISBN 10: 9819953324 ISBN 13: 9789819953325

Casa editrice: Springer Nature, 2023

Riassunto

This book demonstrates different methods (as well as real-life examples) of handling uncertainty like probability and Bayesian theory, Dempster-Shafer theory, certainty factor and evidential reasoning, fuzzy logic-based approach, utility theory and expected utility theory. At the end, highlights will be on the use of these methods which can help to make decisions under uncertain situations. This book assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation. The book is comprehensive, but it prohibits unnecessary mathematics.

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Informazioni sull?autore

JYOTISMITA CHAKI, PhD. is an Associate Professor in School of Computer Science and Engineering at Vellore Institute of Technology, Vellore, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Artificial Intelligence and Machine learning. She has authored and edited many international conferences, journal papers and books. Currently she is the editor of Engineering Applications of Artificial Intelligence Journal, Elsevier, academic editor of PLOS ONE journal and associate editor of Array journal, Elsevier, IET Image Processing, Applied Computational Intelligence and Soft Computing and Machine Learning with Applications journal, Elsevier.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 9819953324
• ISBN 13: 9789819953325
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 116
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania