Alberto Rodrigues da Silva (ed.); Miguel Mira da Silva (ed.); Jacinto Estima (ed.); Chris Barry (ed.); Michael Lang (ed.) Springer (2024) Kovakantinen kirja
Alberto Rodrigues da Silva; Miguel Mira da Silva; Jacinto Estima; Chris Barry; Michael Lang; Henry Linger; Chri Schneider Springer International Publishing AG (2024) Pehmeäkantinen kirja
Luís Filipe Rosário Lucas; Eduardo Antônio Barros da Silva; Sérgio Manuel Maciel de Faria; Nuno Miguel Morais Rodrigues; Lib Springer (2017) Kovakantinen kirja
Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.
