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[公告]交通大學巨量資料探勘短期課程

張貼者:2012年9月13日 下午10:54islabFJU   [ 已更新 2012年9月13日 下午10:55 ]
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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