2023 - Research.com Computer Science in Australia Leader Award
Ivor W. Tsang focuses on Artificial intelligence, Pattern recognition, Support vector machine, Kernel method and Machine learning. His study in Neural coding, Contextual image classification, Feature, Training set and Kernel falls within the category of Artificial intelligence. His Pattern recognition study frequently draws connections between related disciplines such as Transfer of learning.
Within one scientific family, Ivor W. Tsang focuses on topics pertaining to Algorithm under Support vector machine, and may sometimes address concerns connected to Efficiency. His Kernel method study combines topics in areas such as Time complexity and Mathematical optimization. His work focuses on many connections between Machine learning and other disciplines, such as Classifier, that overlap with his field of interest in TRECVID.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Support vector machine and Algorithm. His study involves Classifier, Deep learning, Kernel method, Feature extraction and Multiple kernel learning, a branch of Artificial intelligence. His Kernel method research integrates issues from Quadratic programming and Mathematical optimization.
Many of his research projects under Machine learning are closely connected to Generalization with Generalization, tying the diverse disciplines of science together. His Pattern recognition research incorporates themes from Contextual image classification and Cluster analysis. The study incorporates disciplines such as Margin and Image retrieval in addition to Support vector machine.
His primary areas of study are Artificial intelligence, Machine learning, Deep learning, Theoretical computer science and Generalization. The various areas that Ivor W. Tsang examines in his Artificial intelligence study include Computer vision and Pattern recognition. Pattern recognition is represented through his Discriminative model and Support vector machine research.
His research investigates the connection with Machine learning and areas like Rate of convergence which intersect with concerns in Privacy preserving, Mathematical optimization, Proximal Gradient Methods and Multiple kernel learning. He interconnects Early stopping, Training set, Speech recognition, Multi label learning and Focus in the investigation of issues within Deep learning. His Theoretical computer science study which covers Benchmark that intersects with Search engine indexing, Multimedia database and Spectral clustering.
His scientific interests lie mostly in Artificial intelligence, Feature learning, Machine learning, Pattern recognition and Deep learning. As a part of the same scientific family, Ivor W. Tsang mostly works in the field of Artificial intelligence, focusing on Computer vision and, on occasion, Hallucinating. His Feature learning study integrates concerns from other disciplines, such as Graph classification, Android malware, Malware and Upload.
His work on Artificial neural network as part of general Machine learning research is frequently linked to Rank, thereby connecting diverse disciplines of science. His Mutual information study, which is part of a larger body of work in Pattern recognition, is frequently linked to Infomax, bridging the gap between disciplines. Ivor W. Tsang has researched Deep learning in several fields, including Training set and Robustness.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Domain Adaptation via Transfer Component Analysis
Sinno Jialin Pan;Ivor W Tsang;James T Kwok;Qiang Yang.
IEEE Transactions on Neural Networks (2011)
Core Vector Machines: Fast SVM Training on Very Large Data Sets
Ivor W. Tsang;James T. Kwok;Pak-Ming Cheung.
Journal of Machine Learning Research (2005)
The pre-image problem in kernel methods
J.T.-Y. Kwok;I.W.-H. Tsang.
IEEE Transactions on Neural Networks (2004)
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han;Quanming Yao;Xingrui Yu;Gang Niu.
neural information processing systems (2018)
Visual event recognition in videos by learning from web data
Lixin Duan;Dong Xu;Ivor Wai-Hung Tsang;Jiebo Luo.
computer vision and pattern recognition (2010)
Local features are not lonely – Laplacian sparse coding for image classification
Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia;Peilin Zhao.
computer vision and pattern recognition (2010)
Domain Transfer Multiple Kernel Learning
Lixin Duan;I. W. Tsang;Dong Xu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
Feiping Nie;Dong Xu;Ivor Wai-Hung Tsang;Changshui Zhang.
IEEE Transactions on Image Processing (2010)
Kernel sparse representation for image classification and face recognition
Shenghua Gao;Ivor Wai-Hung Tsang;Liang-Tien Chia.
european conference on computer vision (2010)
Improved Nyström low-rank approximation and error analysis
Kai Zhang;Ivor W. Tsang;James T. Kwok.
international conference on machine learning (2008)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: