2017 - IEEE Fellow For contributions to computational algorithms for kernel methods
James T. Kwok mainly focuses on Artificial intelligence, Pattern recognition, Support vector machine, Machine learning and Kernel method. His Artificial intelligence study typically links adjacent topics like Computer vision. In the subject of general Pattern recognition, his work in Feature extraction, Wavelet transform and Feature vector is often linked to Domain, thereby combining diverse domains of study.
His study looks at the relationship between Support vector machine and fields such as Quadratic programming, as well as how they intersect with chemical problems. The concepts of his Machine learning study are interwoven with issues in Class and Categorization. James T. Kwok works mostly in the field of Kernel method, limiting it down to topics relating to Algorithm and, in certain cases, Mathematical optimization, Theoretical computer science and Rate of convergence, as a part of the same area of interest.
James T. Kwok spends much of his time researching Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Mathematical optimization. His Artificial intelligence study often links to related topics such as Data mining. James T. Kwok studied Algorithm and Matrix that intersect with Singular value decomposition.
His work on Semi-supervised learning as part of general Machine learning research is frequently linked to Space, thereby connecting diverse disciplines of science. His Mathematical optimization research incorporates elements of Time complexity, Regularization and Convex function. His work on Radial basis function kernel as part of general Kernel method study is frequently linked to Gaussian function, bridging the gap between disciplines.
James T. Kwok mostly deals with Artificial intelligence, Algorithm, Mathematical optimization, Machine learning and Matrix. His Artificial intelligence study combines topics in areas such as Sample and Pattern recognition. His Algorithm research incorporates themes from Rate of convergence and Matrix norm.
His Mathematical optimization study integrates concerns from other disciplines, such as Convex function and Stochastic gradient descent. James T. Kwok combines subjects such as Bayesian probability and Taxonomy with his study of Machine learning. His biological study spans a wide range of topics, including Regularization, Convolution, Frequency domain and Neural coding.
His primary areas of study are Artificial intelligence, Algorithm, Machine learning, Mathematical optimization and Matrix. His studies in Artificial intelligence integrate themes in fields like Matrix decomposition and Optimization problem. His work carried out in the field of Algorithm brings together such families of science as Matrix norm and Matrix completion.
His Machine learning study incorporates themes from Categorization and Taxonomy. The Mathematical optimization study combines topics in areas such as Stochastic gradient descent and Variance reduction. His Matrix research is multidisciplinary, incorporating elements of Regularization and Key.
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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)
Transfer learning via dimensionality reduction
Sinno Jialin Pan;James T. Kwok;Qiang Yang.
national conference on artificial intelligence (2008)
The pre-image problem in kernel methods
J.T.-Y. Kwok;I.W.-H. Tsang.
IEEE Transactions on Neural Networks (2004)
Combination of images with diverse focuses using the spatial frequency
Shutao Li;Shutao Li;James Tin-Yau Kwok;Yaonan Wang.
Information Fusion (2001)
Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images
Shutao Li;Shutao Li;James T Kwok;Yaonan Wang.
Information Fusion (2002)
Improved Nyström low-rank approximation and error analysis
Kai Zhang;Ivor W. Tsang;James T. Kwok.
international conference on machine learning (2008)
Multifocus image fusion using artificial neural networks
Shutao Li;James T. Kwok;Yaonan Wang.
Pattern Recognition Letters (2002)
Mining customer product ratings for personalized marketing
Kwok-Wai Cheung;James T. Kwok;Martin H. Law;Kwok-Ching Tsui.
decision support systems (2003)
Asynchronous Distributed ADMM for Consensus Optimization
Ruiliang Zhang;James Kwok.
international conference on machine learning (2014)
Profile was last updated on December 6th, 2021.
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