Advances in Partitioning Techniques: A Prospective Towards Artificial Intelligence
暫譯: 分割技術的進展：面向人工智慧的前景
Guggari, Shankru, V, Umadevi, Kadappa, Vijayakumar
相關主題
商品描述
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.
商品描述(中文翻譯)
本書探討了針對傳統機器學習演算法的各種分割策略。這本書是一本重要的資源，照亮了釋放分割在塑造人工智慧技術未來的全部潛力的道路。
作者簡介
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.
作者簡介(中文翻譯)
Shankru Guggari 是一位機器學習專家，主要專注於提升機器學習技術的性能。他的研究興趣包括模式識別、可解釋的人工智慧（explainable AI）和機器學習。他在多個國際會議和期刊上發表了研究成果，並擁有超過四年的學術經驗。
Umadevi V，來自印度理工學院馬德拉斯分校的博士，是班加羅爾BMS工程學院的計算機科學教授及資深IEEE會員。她在知名期刊和會議上發表了大量論文，並獲得醫療熱成像研究的資助。
Vijaya Kumar Kadappa 於2010年在海得拉巴中央大學獲得博士學位，目前擔任班加羅爾BMS工程學院計算機應用系的教授。他擁有超過30篇的研究出版物。Kadappa是IUPR-AI、ISTE和CSI的終身會員。
