Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)

Chapter: Pedro Domingos A General Framework for Mining Massive Data Streams

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Suggested Citation: "Pedro Domingos A General Framework for Mining Massive Data Streams ." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.

Pedro Domingos

A General Framework for Mining Massive Data Streams

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Technical Paper

BIOSKETCH: Pedro Domingos is a professor in the department of computer science and engineering at the University of Washington. He received a master’s degree in electrical engineering and computer science in 1992 from the Institute Superior Técnica (IST) in Lisbon and a second master’s degree in 1994 and a PhD in 1997 in information and computer science from the University of California at Irvine. He spent 2 years as an assistant professor at IST before joining the faculty of the University of Washington in 1999.

Dr. Domingos is the author or coauthor of over 100 technical publications in topics related to machine learning and data mining. He is also the associate editor of JAIR, a member of the editorial board of Machine Learning, and a cofounder of the International Machine Learning Society. Dr. Domingos was program co-chair of KDD-2003, and has served on the program committees of American Association for Artificial Intelligence (AAAI), International Conference on Machine Learning (ICML), International Joint Conferences on Artificial Intelligence (IJCAI), Knowledge Discovery and Data Mining (KDD), the World Wide Web Consortium (WWW), and others. He has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, two best paper awards at KDD, and other distinctions.

Suggested Citation: "Pedro Domingos A General Framework for Mining Massive Data Streams ." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.
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