Leseprobe
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Modeling and Optimization of Signals Using Machine Learning Techniques
Chandra Singh (Hrsg.), Rathishchandra R. Gatti (Hrsg.), Naveen Kumar S. (Hrsg.), Manjunatha Badiger (Hrsg.), Varun Saxena (Hrsg.), K.V.S.S.S.S. Sairam (Hrsg.)
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Naturwissenschaften, Medizin, Informatik, Technik / Allgemeines, Lexika
Beschreibung
Explore the power of machine learning to revolutionize signal processing and optimization with cutting-edge techniques and practical insights in this outstanding new volume from Scrivener Publishing.
Modeling and Optimization of Signals using Machine Learning Techniques is designed for researchers from academia, industries, and R&D organizations worldwide who are passionate about advancing machine learning methods, signal processing theory, data mining, artificial intelligence, and optimization. This book addresses the role of machine learning in transforming vast signal databases from sensor networks, internet services, and communication systems into actionable decision systems. It explores the development of computational solutions and novel models to handle complex real-world signals such as speech, music, biomedical data, and multimedia.
Through comprehensive coverage of cutting-edge techniques, this book equips readers with the tools to automate signal processing and analysis, ultimately enhancing the retrieval of valuable information from extensive data storage systems. By providing both theoretical insights and practical guidance, the book serves as a comprehensive resource for researchers, engineers, and practitioners aiming to harness the power of machine learning in signal processing.
Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
Kundenbewertungen
Clustering, Signal Processing, Signal Modeling, Dimensionality Reduction, Optimization, Supervised Learning, Unsupervised Learning, Adaptive Signal Processing, Reinforcement Learning, Signal Classification, Principal Component Analysis, Neural Networks, Regression, Telecommunications, Machine Learning