Entries tagged with Sigkdd
Prof. Domingos carried out some of the earliest research on mining data streams. His VFDT algorithm was the first to be capable of learning decision trees from streams while guaranteeing that the result is very close to that of batch learning, and remains the fastest decision tree learner available. He went on to generalize the ideas in VFDT to clustering, the EM algorithm, Bayesian network structure learning, and other problems. The resulting VFML toolkit is one of the best open-source resources for stream mining.
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2014 SIGKDD Doctoral Dissertation Award
CALL FOR NOMINATIONS
Nomination Deadline: April 30, 2014
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This annual award introduced in 2008 recognizes excellent research by This annual award by ACM SIGKDD will |
ACM SIGKDD invites submissions and sponsorships for KDD 2014
August 24-27, 2014, New York City, USA
Welcome to KDD 2014, an interdisciplinary conference that brings together researchers and practitioners from all aspects of data mining, knowledge discovery, and
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The ACM SIGKDD election results were recently concluded. The new SIGKDD leadership team took office on July 1, 2013. The newly elected chair of SIGKDD is Prof. Bing Liu, professor at University of Illinois at Chicago. Prof. Liu is a well respected data mining and machine learning researcher and
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SIGKDD Welcomes New Chair, Treasurer and Members to Its Executive Committee
The ACM SIGKDD election results were recently concluded. The new SIGKDD leadership team took office on July 1, 2013. The newly elected chair of SIGKDD is Prof. Bing Liu, professor at University of Illinois at Chicago.
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ACM SIGKDD is pleased to announce that Prof. Jon Kleinberg is the winner of the 2013 Innovation Award. He is recognized for his seminal contributions to the analysis of social and information networks, mining the web graph, study of cascading behaviors in networks, and the development of
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ACM SIGKDD is pleased to announce that Dr. Gabor Melli is the winner of its 2013 Service Award. Dr. Gabor Melli is recognized for his substantial technical contributions to the practice and application of data mining and for his outstanding service to the global KDD community.
ACM SIGKDD Service
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Practice Makes Perfect: For a third consecutive year, the KDD Industry Practice Expo (IPE) track brings together the leading data science practitioners
The Industry Practice Expo (IPE) track at KDD 2013 brings together leading industry and government practitioners who |
