I
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
More filters
Journal ArticleDOI
Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions
TL;DR: This paper builds an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data and employs a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces.
Proceedings Article
Transfer learning for cross-language text categorization through active correspondences construction
TL;DR: This paper presents a general framework to integrate active learning to construct correspondences between heterogeneous domains for HTL, namely HTL through active correspondences construction (HTLA) and develops a new HTL method.
Proceedings ArticleDOI
High-order Proximity Preserving Information Network Hashing
TL;DR: The results demonstrate that INH-MF can perform significantly better than competing learning to hash baselines in both tasks, and surprisingly outperforms network embedding methods, including DeepWalk, LINE and NetMF, in the task of node recommendation.
Journal Article
Efficient hyperkernel learning using second-order cone programming
Ivor W. Tsang,James T. Kwok +1 more
TL;DR: Wang et al. as discussed by the authors proposed a second-order cone program (SOCP) to learn the kernel function directly in an inductive setting, which can then be solved more efficiently than SDPs.
Journal ArticleDOI
Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks
Biswajeet Pradhan,Husam Abdulrasool H. Al-Najjar,Maher Ibrahim Sameen,Ivor W. Tsang,Abdullah Al-Amri +4 more
TL;DR: This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos and demonstrated that the proposed ZSL is a promising tool forLand cover mapping based on high- resolution photos.