棰樼洰: Concept Drift Detection and Adaptation
銆€銆€涓昏浜猴細Distinguished Professor Jie Lu
銆€銆€鏃墮棿錛?017騫?1鏈?7鏃ワ紙鍛ㄤ竴錛?10:00
銆€銆€鍦扮偣錛氭牸鑷翠腑妤?00瀹?br />銆€銆€鎶ュ憡浜虹畝浠? Distinguished Professor Jie Lu is an internationally renowned scientist in the areas of computational intelligence, specifically in decision support systems, fuzzy transfer learning, concept drift, and recommender systems. She is the Associate Dean in Research Excellence in the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS) and the Director of Centre for Artificial Intelligence (CAI) at UTS. She is also the co-Director of the Joint Research Centre Wise Information Systems (WIS) between UTS and Shanghai University. She has published six research books and 400 papers in Artificial Intelligence, IEEE transactions on Fuzzy Systems and other refereed journals and conference proceedings (H-index 43, Google Scholar). She has won eight Australian Research Council (ARC) discovery grants and 10 other research grants for over $4 million. She serves as Editor-In-Chief for Knowledge-Based Systems (Elsevier) and Editor-In-Chief for International Journal on Computational Intelligence Systems (Atlantis), has delivered 15 keynote speeches at international conferences, and has chaired 10 international conferences. She is an ARC panel member (2016-2018) and Fellow of IFSA.
銆€銆€鎶ュ憡鍐呭錛欳oncept Drift is known as unforeseeable change in underlying streaming data distribution over time. The phenomenon of concept drift has been recognized as the root cause of decreased effectiveness in many decision-related applications. Adaptive learning under concept drift is a relatively new research field and is one of the most pressing and fundamental problems in the current age of big data. Building an adaptive system is a highly promising solution for coping with persistent environmental change and avoiding system performance degradation. This talk will present a set of methods and algorithms that can effectively and accurately detect concept drift and react to it, with knowledge adaptation, in a timely way.
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鐞嗗闄?br />2017騫?1鏈?3鏃?/p>
