日期與地點：2012年10月3日，交通大學電子資訊大樓第四會議室 報名費用：1500元、1000元(中華民國人工智慧學會成員) 演講資訊： Talk 1：Managing and Mining Big Graph Data Spearker：Dr. Haixun Wang (Microsoft Research Asia) Abstract：We are facing challenges at all levels ranging from infrastructures to programming models for managing and mining large graphs. A lot of algorithms on graphs are ad-hoc in the sense that each of them assumes that the underlying graph data can be organized in a certain way that maximizes the performance of the algorithm. In other words, there is no standard graph systems based on which graph algorithms are developed and optimized. In response to this situation, a lot of graph systems have been proposed recently. In this tutorial, we discuss several representative systems. Still, we focus on providing perspectives from a variety of standpoints on the goals and the means for developing a general purpose graph system. We highlight the challenges posed by the graph data, the constraints of architectural design, the different types of application needs, and the power of different programming models that support such needs. Talk 2：On Matching Web-scale Entity Graphs Spearker：Prof. Seung-won Hwang (POSTECH, Korea) Abstract：This talk introduces the problem of matching web-scale entity graphs, such as multilingual name graphs and social network graphs, as novel data-rich solutions to difficult tasks such as name translation or social id finding. We will show how these data-rich solutions outperform existing model-rich state-of-the-arts. We present our evaluation results using real-life entity graphs and discuss future directions. Talk 3：Bridging Machine Learning Theory and Practice – What we have learned from participating ACM KDD CUP Speaker：Prof. Shou-de Lin (National Taiwan University) Abstract：While it is possible to learn a variety of machine learning and data mining theories from lectures or books, applying them effectively and efficiently to the real-world data is a completely different story. Very often data miners have to suffer a painful process of trial and error in applying machine learning tools due to lack of experience. Dealing with the practical issues on data is rather an art than science. Nevertheless, in this talk I’ll share some of the experiences we have learned from participating ACM KDDCup for the past several years. Instead of theoretical machine learning techniques, this talk will be focused more on the practical issues and tricks that are important to train an effective and efficient classifier, based on several case studies about medical data mining, telcom user behavior mining, educational data mining, and music recommendation. |
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