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Full-Text Articles in Physical Sciences and Mathematics
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Research Collection School Of Computing and Information Systems
We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last …
Augmenting Online Learning With Real-Time Conferencing: Experiences From An International Course, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Augmenting Online Learning With Real-Time Conferencing: Experiences From An International Course, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
This paper reports experiences from the use of real-time conferencing to support synchronous class interaction in an international online course. Through combination of video, audio, application sharing and chat, the students and instructors engaged in weekly interactions in a virtual classroom. This created an environment for rich interaction, augmenting the traditional use of course repositories. Further, this gave the students hands-on experience with real-time conferencing tools which are increasingly common in the workplace. The paper also discusses experienced challenges related to combining the use of multiple synchronous communication channels and presents implications for further use of real-time conferencing in online …
Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated in order to reflect the influence of the added support vector. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short, that explicitly addresses this problem. …