Title: Online Learning for Big Data Mining
This tutorial was presented at SDM2013, Austin Texas, USA on May 4 (Saturday), 2013.
Slides
Speaker
Dr Steven C.H. Hoi
School of Computer Engineering,
Nanyang Technological University
Abstract
No doubt we are entering the age of Big Data today. For example, in social media domain, massive amounts of data are being generated by billions of users through various social media sharing tools. The advent of big data presents a number of challenges and opportunities for research and development of scalable machine learning and data mining techniques. In this tutorial, I will first introduce the motivation and background of big data mining, and then focus on presenting the family of classical and latest online learning methods and algorithms, which are promising to tackle emerging challenges of mining big data in many real-world applications. The main content of this tutorial consists two parts: (i) online learning methods for linear classification, and (ii) kernel-based online learning methods for nonlinear classification.
Coverage
Introduction
• Big Data Mining: Opportunities & Challenges
• Online Learning: What, Why, Where
Online Learning
• Overview
• Traditional Linear Online Learning
• Non-traditional Linear Online Learning
• Kernel-based Online Learning
• Online Multiple Kernel Learning
Discussions + Q&A
Bio of Speaker
Speaker: Steven C. H. Hoi
Dr Steven C.H. Hoi is currently an Assistant Professor of the School of Computer Engineering at Nanyang Technological University, Singapore. He received his Bachelor degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 2002, and both his Master and PhD degrees in Computer Science and Engineering from the Chinese University of Hong Kong, in 2004 and 2006, respectively. His research interests are machine learning and data mining and their applications to tackle challenges of real-world problems in several domains, including multimedia information retrieval (image and video search), social media, web search and data mining, computer vision and pattern recognition, bioinformatics, and computational finance. He has published over 100 referred papers in premier international journals and conferences in his research areas. He is fairly active in his research communities. In particular, he had served as General Co-chair for ACM SIGMM Workshops on Social Media (WSM'09, WSM'10, WSM'11), Program Co-Chair for the fourth Asian Conference on Machine Learning (ACML'12), Book editor for "Social Media Modeling and Computing", Guest editor for Machine Learning journal and ACM Transactions on Intelligent Systems and Technology, Area Chair/Senior Program Committee for conferences including ACM Multimedia 2012 and ACML'11, Technical PC member for many international conferences, and technical referee for top journals and magazines. He had been invited for external funds review by worldwide funding agencies, including the USA NSF funding agency, Hong Kong RGC funding agency, and some European funding agency. He is a member of ACM, AAAI, and IEEE.
