時段: 2010/05/26 10:00a.m.-11:00a.m.

地點:資工系4204室

題目: Interactively Building Mashups by Demonstration 

主講者:Dr. Craig A. Knoblock

服務單位: University of Southern California 

內容摘要:

There are a number of tools and services available now for building mashups on the Web.  However, many of the tools for constructing mashups reply on a widget paradigm, where users must select, customize, and connect widgets to build the desired application.  While this approach does not require programming, the users must still understand programming concepts to successfully create a mashup.   In this talk I describe our programming-by-demonstration approach to building mashups by example.  Instead of requiring a user to select and customize a set of widgets, the user simply demonstrates the integration task by example.  I will describe how this approach addresses the problems of extracting data from various sources, cleaning and modeling the extracted data, integrating the data across sources, and visualizing the integrated results in a geospatial context. We implemented these ideas in a system called Karma and evaluated Karma on a set of 20 users and showed that compared to other mashup construction tools, Karma allowed more of the users to successfully build mashups and made it possible to build these mashups significantly faster compared to using a widget-based approach. 

個人簡介:

Craig Knoblock is a Senior Project Leader at the Information Sciences Institute, a unit of the University of Southern California (USC), and a Research Professor in the USC Viterbi School of Engineering’s Computer Science Department.  Dr. Knoblock also is a founder and Chief Scientist of Fetch Technologies, a web integration solutions provider, and of Geosemble Technologies, which develops geospatial information solutions.

At the Information Sciences Institute (ISI), Dr. Knoblock leads a team of about 20 researchers, staff and students in developing intelligent techniques for rapid, efficient information integration.  He focuses on constructing distributed, integrated applications from online sources ñ through information extraction, source modeling, record linkage, constraint reasoning and other technologies for geospatial and bioinformatics data integration.

Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Distinguished Scientist of the Association of Computing Machinery (ACM), a Trustee of the International Joint Conference on Artificial Intelligence (IJCAI), and past President of the International Conference on Automated Planning and Scheduling (ICAPS).  He has served on the Senior Program Committee of the National Artificial Intelligence Conference, among others, and is conference chair for the 2011 International Joint Conference on AI (IJCAI). 

Dr. Knoblock has published Generating Abstraction Hierarchies (Kluwer Academic Publishers, 1993), along with more than 200 journal articles, book chapters and conference papers.  He serves on the Editorial Boards of several journals, including Artificial Intelligence and Computational Intelligence.  He also has been the primary advisor for more than a dozen Ph.d. students.

Dr. Knoblock was awarded his Bachelor of Science degree by Syracuse University, and his Master’s and Ph.D. by Carnegie Mellon University, all in computer science.   He resides in Los Angeles, where he commutes by bicycle and referees kids soccer games.

時段: 2010/05/26 11:00a.m.-12:00a.m.

地點:資工系4204室

題目: Transfer Learning with Applications 

主講者: Qiang Yang

服務單位: Hong Kong University of Science and Technology 

內容摘要:

Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces.  We can find many novel applications of machine learning and data mining where transfer learning is necessary.  In this talk, I will give an introduction to transfer learning and then highlight some important applications such as text and image classification, sensor network data mining and activity recognition, collaborative filtering and bioinformatics.  I will also discuss some potential future directions of transfer learning. 

個人簡介:
Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. His research interests are artificial intelligence, including automated planning, machine learning and data mining. He graduated from Peking University in 1982 with BSc. in Astrophysics, and obtained his MSc. degrees in Astrophysics and Computer Science from the University of Maryland, College Park in 1985 and 1987, respectively.  He obtained his PhD in Computer Science from the University of Maryland, College Park in 1989 and became an assistant/associate professor at the University of Waterloo between 1989 and 1995.  He was a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001. 

Qiang Yang has been active in research on artificial intelligence planning, machine learning and data mining. His research teams won the 2004 and 2005 ACM KDDCUP international competitions on data mining.  He has been on several editorial boards of international journals, including the editor in chief for ACM TIST (http://tist.acm.org), IEEE Intelligent Systems, IEEE Transactions on Knowledge and Data Engineering and Web Intelligence. He has been an organizer for several international conferences in AI and data mining, including being the program co-chair for ACM KDD2010, conference co-chair for ACM IUI 2010 and ICCBR 2001, program co-chair for PRICAI 2006 and PAKDD 2007, workshop chair for ACM KDD 2007, AAAI tutorial chair for AAAI 2005 and 2006, data mining contest chair for IEEE ICDM 2007 and 2009, and vice chair for ICDM 2006 and CIKM 2009. He is a fellow of IEEE and a member of AAAI and ACM. His home page is athttp://www.cse.ust.hk/~qyang