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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.
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The Emerging Trends of Multi-Label Learning
TL;DR: There has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data, and it is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.
Proceedings Article
Spectral embedded clustering
TL;DR: A new spectral clustering method, referred to as Spectral Embedded Clustering (SEC), to minimize the normalized cut criterion in spectral clusters as well as control the mismatch between the cluster assignment matrix and the low dimensional embedded representation of the data.
Journal ArticleDOI
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions
TL;DR: A hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper, integrated into the context of the PSOs to improve the particles' local search ability.
Journal ArticleDOI
Multilabel Prediction via Cross-View Search
TL;DR: A formulation for multilabel learning, from the perspective of cross-view learning, that explores the correlations between the input and the output, and jointly learns a semantic common subspace and view-specific mappings within one framework.
Journal ArticleDOI
Making decision trees feasible in ultrahigh feature and label dimensions
Weiwei Liu,Ivor W. Tsang +1 more
TL;DR: A novel data-dependent generalization error bound for the perceptron decision tree (PDT), which provides the theoretical justification to learn a sparse linear hyperplane in each decision node and to prune the tree, and a sparse coding tree framework for multi-label annotation problems and provide the theoretical analysis.