H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 61 Citations 17,386 185 World Ranking 1449 National Ranking 138

Research.com Recognitions

Awards & Achievements

2017 - IEEE Fellow For contributions to computational algorithms for kernel methods

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

His most cited work include:

  • Domain Adaptation via Transfer Component Analysis (1893 citations)
  • Core Vector Machines: Fast SVM Training on Very Large Data Sets (792 citations)
  • Transfer learning via dimensionality reduction (457 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (53.87%)
  • Pattern recognition (32.04%)
  • Algorithm (23.59%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (53.87%)
  • Algorithm (23.59%)
  • Mathematical optimization (20.07%)

In recent papers he was focusing on the following fields of study:

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.

Between 2015 and 2021, his most popular works were:

  • Generalizing from a Few Examples: A Survey on Few-shot Learning (197 citations)
  • Multi-Label Learning with Global and Local Label Correlation (79 citations)
  • Loss-aware Binarization of Deep Networks (78 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

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.

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.

Top Publications

Domain Adaptation via Transfer Component Analysis

Sinno Jialin Pan;Ivor W Tsang;James T Kwok;Qiang Yang.
IEEE Transactions on Neural Networks (2011)

1935 Citations

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)

1218 Citations

Transfer learning via dimensionality reduction

Sinno Jialin Pan;James T. Kwok;Qiang Yang.
national conference on artificial intelligence (2008)

580 Citations

The pre-image problem in kernel methods

J.T.-Y. Kwok;I.W.-H. Tsang.
IEEE Transactions on Neural Networks (2004)

554 Citations

Combination of images with diverse focuses using the spatial frequency

Shutao Li;Shutao Li;James Tin-Yau Kwok;Yaonan Wang.
Information Fusion (2001)

416 Citations

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)

391 Citations

Improved Nyström low-rank approximation and error analysis

Kai Zhang;Ivor W. Tsang;James T. Kwok.
international conference on machine learning (2008)

390 Citations

Multifocus image fusion using artificial neural networks

Shutao Li;James T. Kwok;Yaonan Wang.
Pattern Recognition Letters (2002)

383 Citations

Mining customer product ratings for personalized marketing

Kwok-Wai Cheung;James T. Kwok;Martin H. Law;Kwok-Ching Tsui.
decision support systems (2003)

370 Citations

Asynchronous Distributed ADMM for Consensus Optimization

Ruiliang Zhang;James Kwok.
international conference on machine learning (2014)

352 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Top Scientists Citing James T. Kwok

Ivor W. Tsang

Ivor W. Tsang

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