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 35 Citations 8,286 341 World Ranking 7435 National Ranking 210

Overview

What is he best known for?

The fields of study Dinh Phung is best known for:

  • Duration (music)
  • Rhythm
  • Machine learning

His Machine learning study typically links adjacent topics like Stability (learning theory), Decision tree and Feature selection. He performs integrative study on Stability (learning theory) and Machine learning. Dinh Phung integrates many fields in his works, including Feature selection and Data mining. He conducts interdisciplinary study in the fields of Data mining and Decision tree through his works. He integrates many fields, such as Social science, Multidisciplinary approach and Social environment, in his works. He merges many fields, such as Multidisciplinary approach and Social science, in his writings. His study on Statistics is interrelated to topics such as Flexibility (engineering) and Delphi method. Flexibility (engineering) is closely attributed to Statistics in his work. His Artificial intelligence study frequently draws connections between related disciplines such as Pattern recognition (psychology).

His most cited work include:

  • Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View (467 citations)
  • Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso (59 citations)
  • Sensing and using social context (38 citations)

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

His Machine learning research is linked to Transfer of learning, Feature selection, Stability (learning theory) and Cluster analysis, among other subjects. Dinh Phung brings together Stability (learning theory) and Machine learning to produce work in his papers. Cluster analysis and Data mining are two areas of study in which Dinh Phung engages in interdisciplinary research. In his articles, he combines various disciplines, including Data mining and Feature selection. Much of his study explores World Wide Web relationship to Social media and Lasso (programming language). Dinh Phung undertakes multidisciplinary studies into Social media and World Wide Web in his work. His Artificial intelligence study frequently intersects with other fields, such as Bayesian probability. His studies link Artificial intelligence with Bayesian probability. Dinh Phung conducts interdisciplinary study in the fields of Data science and Big data through his works.

Dinh Phung most often published in these fields:

  • Artificial intelligence (70.59%)
  • Data mining (64.71%)
  • Machine learning (58.82%)

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

Activity recognition and abnormality detection with the switching hidden semi-Markov model

T.V. Duong;H.H. Bui;D.Q. Phung;S. Venkatesh.
computer vision and pattern recognition (2005)

737 Citations

Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

Ba-Ngu Vo;Ba-Tuong Vo;Dinh Phung.
IEEE Transactions on Signal Processing (2014)

632 Citations

Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

N.T. Nguyen;D.Q. Phung;S. Venkatesh;H. Bui.
computer vision and pattern recognition (2005)

479 Citations

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

Wei Luo;Dinh Phung;Truyen Tran;Sunil Gupta.
Journal of Medical Internet Research (2016)

441 Citations

A novel embedding model for knowledge base completion based on convolutional neural network

Dai Quoc Nguyen;Tu Dinh Nguyen;Dat Quoc Nguyen;Dinh Q. Phung.
north american chapter of the association for computational linguistics (2018)

376 Citations

Predicting healthcare trajectories from medical records: A deep learning approach.

Trang Pham;Truyen Tran;Dinh Q. Phung;Svetha Venkatesh.
Journal of Biomedical Informatics (2017)

326 Citations

DeepCare: A Deep Dynamic Memory Model forźPredictive Medicine

Trang Pham;Truyen Tran;Dinh Phung;Svetha Venkatesh.
knowledge discovery and data mining (2016)

263 Citations

Affective and Content Analysis of Online Depression Communities

Thin Nguyen;Dinh Phung;Bo Dao;Svetha Venkatesh.
IEEE Transactions on Affective Computing (2014)

205 Citations

Dual discriminator generative adversarial nets

Tu Dinh Nguyen;Trung Le;Hung Vu;Dinh Q. Phung.
neural information processing systems (2017)

203 Citations

MGAN: Training Generative Adversarial Nets with Multiple Generators

Quan Hoang;Tu Dinh Nguyen;Trung Le;Dinh Phung.
international conference on learning representations (2018)

197 Citations

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