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Ivor W. Tsang
Researcher at University of Technology, Sydney
Publications - 361
Citations - 22076
Ivor W. Tsang is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 64, co-authored 322 publications receiving 18649 citations. Previous affiliations of Ivor W. Tsang include Hong Kong University of Science and Technology & Agency for Science, Technology and Research.
Papers
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Proceedings ArticleDOI
Maximum Margin/Volume Outlier Detection
Shukai Li,Ivor W. Tsang +1 more
TL;DR: This paper first proposes to use the maximum margin criterion to sift unknown outliers from a given data set, which demonstrates superior performance in outlier detection, and proposes an effective procedure to find a largely violated labeling vector for identifying rare outlier from abundant normal patterns.
Posted Content
A Split-Merge Framework for Comparing Clusterings
TL;DR: In this paper, the authors model the relation between two clusterings as a bipartite graph and propose a general component-based decomposition formula based on the components of the graph.
Posted Content
Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels
TL;DR: Pumpout is proposed as a meta approach to learning with noisy labels and an alternative to early stopping, and it is demonstrated via experiments that Pumpout robustifies two representative base learning methods, and the performance boost is often significant.
Book ChapterDOI
Discovering User Interests from Social Images
TL;DR: A hybrid mixture model for user interests discovery which exploits both the textual and visual content associated with social images and allows the semantic interpretation of user interests in both the visual and textual perspectives is proposed.
Proceedings Article
Learning sparse confidence-weighted classifier on very high dimensional data
TL;DR: This paper presents an online-batch CW learning scheme, and then presents a novel paradigm to learn sparse CW classifiers, which essentially identifies feature groups and naturally builds a block diagonal covariance structure, making it very suitable for CW learning over very high-dimensional data.