D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 8,229 135 World Ranking 7883 National Ranking 3679

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Glenn Fung mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Support vector machine and Structured support vector machine. As part of his studies on Artificial intelligence, he often connects relevant areas like Stochastic process. His Pattern recognition research is multidisciplinary, incorporating elements of Random field and Robustness.

His biological study spans a wide range of topics, including Hyperplane and Benchmark. His work deals with themes such as Classifier, Margin, Cluster analysis, Data classification and Test set, which intersect with Support vector machine. His Structured support vector machine research incorporates themes from Least squares support vector machine, Linear classifier, Margin classifier and Relevance vector machine.

His most cited work include:

  • Proximal support vector machine classifiers (717 citations)
  • Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches (273 citations)
  • A Feature Selection Newton Method for Support Vector Machine Classification (261 citations)

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

His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Support vector machine and Data mining. His Artificial intelligence study integrates concerns from other disciplines, such as Linear programming, Hyperplane and Computer vision. His research on Pattern recognition frequently connects to adjacent areas such as Feature.

His Active learning study, which is part of a larger body of work in Machine learning, is frequently linked to Task, bridging the gap between disciplines. The study incorporates disciplines such as Algorithm and Test set in addition to Support vector machine. His work carried out in the field of Linear classifier brings together such families of science as Structured support vector machine and Least squares support vector machine.

He most often published in these fields:

  • Artificial intelligence (56.59%)
  • Pattern recognition (34.11%)
  • Machine learning (24.81%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (56.59%)
  • Machine learning (24.81%)
  • Identification (4.65%)

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

Artificial intelligence, Machine learning, Identification, Customer satisfaction and Recommender system are his primary areas of study. Many of his studies involve connections with topics such as Natural language processing and Artificial intelligence. His study in the field of Incremental learning also crosses realms of Unstructured data.

His research integrates issues of Crowdsourcing, Ground truth, Operational system and Ranking in his study of Identification. His Recommender system study incorporates themes from Bayesian network, Structure learning, Curse of dimensionality, Graphical model and Deep learning. Glenn Fung combines subjects such as Data modeling, Data mining and Missing data with his study of Graphical model.

Between 2014 and 2021, his most popular works were:

  • Predicting readmission risk with institution-specific prediction models (56 citations)
  • An Insurance Recommendation System Using Bayesian Networks (10 citations)
  • Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation (7 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Glenn Fung mainly investigates Artificial intelligence, Identification, Machine learning, Recommender system and Bayesian network. The various areas that he examines in his Artificial intelligence study include Range and Statistics. The concepts of his Identification study are interwoven with issues in Crowdsourcing, Ground truth, Operational system and Metadata.

His Machine learning research includes elements of Data modeling, Inference and Bayesian probability. His Recommender system study incorporates themes from Graphical model and Missing data. Bayesian network and Data mining are frequently intertwined in his study.

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.

Best Publications

Multicategory Proximal Support Vector Machine Classifiers

Glenn M. Fung;O. L. Mangasarian.
Machine Learning (2005)

1371 Citations

Proximal support vector machine classifiers

Glenn Fung;Olvi L. Mangasarian.
knowledge discovery and data mining (2001)

1140 Citations

Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches

Mark Schmidt;Glenn Fung;Rómer Rosales.
european conference on machine learning (2007)

406 Citations

A Feature Selection Newton Method for Support Vector Machine Classification

Glenn M. Fung;O. L. Mangasarian.
Computational Optimization and Applications (2004)

374 Citations

Active Learning from Crowds

Yan Yan;Glenn M. Fung;R mer Rosales;Jennifer G. Dy.
international conference on machine learning (2011)

360 Citations

Semi-superyised support vector machines for unlabeled data classification

Glenn Fung;O. L. Mangasarian.
Optimization Methods & Software (2001)

296 Citations

SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

J. Stoeckel;G. Fung.
international conference on data mining (2005)

250 Citations

Knowledge-Based Support Vector Machine Classifiers

Glenn M. Fung;Olvi L. Mangasarian;Jude W. Shavlik.
neural information processing systems (2002)

238 Citations

Modeling annotator expertise: Learning when everybody knows a bit of something

Yan Yan;Rómer Rosales;Glenn Fung;Mark W. Schmidt.
international conference on artificial intelligence and statistics (2010)

227 Citations

Incremental Support Vector Machine Classification.

Glenn Fung;Olvi L. Mangasarian.
siam international conference on data mining (2002)

214 Citations

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