Information Theory and Machine Learning - Rilegato

Rilegato

ISBN 10: 303655307X ISBN 13: 9783036553078

Casa editrice: MDPI AG, 2022

Riassunto

The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.

Dettagli dell'edizione

• Editore: MDPI AG
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 303655307X
• ISBN 13: 9783036553078
• Rilegatura: Copertina rigida
• Numero di pagine: 254
• Redattore: Zheng Lizhong, Tian Chao
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile