Description
Bridges the gap between theory and practice by providing a general introduction to machine learning that covers a wide range of topics concisely.
About the Author
Masashi Sugiyama received the degrees of Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001, respectively. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received a European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011 and the Young Scientists' Prize from the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan for his contribution to the density-ratio paradigm of machine learning. His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control.
Reviews
"The probabilistic and statistical background is well presented, providing the reader with a complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning." --Zentralblatt MATH
Book Information
ISBN 9780128021217
Author Masashi Sugiyama
Format Paperback
Page Count 534
Imprint Morgan Kaufmann Publishers In
Publisher Elsevier Science & Technology
Weight(grams) 1110g