D-Index & Metrics Best Publications

D-Index & Metrics

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 46 Citations 13,259 362 World Ranking 3339 National Ranking 58

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Electroencephalography and Data mining. His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Signal processing. His Pattern recognition study combines topics from a wide range of disciplines, such as Hilbert–Huang transform and Covariance matrix.

The concepts of his Machine learning study are interwoven with issues in Drug target and Representation. He interconnects Reduction, Entropy, Fractal, Permutation entropy and Epilepsy in the investigation of issues within Electroencephalography. His research integrates issues of Sample entropy and Anesthesia in his study of Entropy.

His most cited work include:

  • Deep convolutional neural networks on multichannel time series for human activity recognition (497 citations)
  • Building text classifiers using positive and unlabeled examples (482 citations)
  • Partially Supervised Classification of Text Documents (410 citations)

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

The scientist’s investigation covers issues in Artificial intelligence, Electroencephalography, Pattern recognition, Machine learning and Neuroscience. He frequently studies issues relating to Data mining and Artificial intelligence. His research in Electroencephalography intersects with topics in Anesthesia, Speech recognition, Audiology and Epilepsy.

Xiaoli Li specializes in Pattern recognition, namely Wavelet. His study in Machine learning focuses on Semi-supervised learning in particular. He mostly deals with Stimulation in his studies of Neuroscience.

He most often published in these fields:

  • Artificial intelligence (37.34%)
  • Electroencephalography (22.45%)
  • Pattern recognition (19.13%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (37.34%)
  • Machine learning (13.06%)
  • Electroencephalography (22.45%)

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

His primary areas of study are Artificial intelligence, Machine learning, Electroencephalography, Pattern recognition and Deep learning. His Artificial intelligence study incorporates themes from Task and Natural language processing. His Natural language processing research includes elements of Frame semantics, Artificial neural network and Representation.

The Leverage, Feature and Transfer of learning research Xiaoli Li does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Association, therefore creating a link between diverse domains of science. Electroencephalography is a primary field of his research addressed under Neuroscience. His Pattern recognition research is multidisciplinary, incorporating elements of Matrix decomposition, Non-negative matrix factorization, Feature, Cognitive impairment and Stability.

Between 2019 and 2021, his most popular works were:

  • Incremental Factorization of Big Time Series Data with Blind Factor Approximation (24 citations)
  • HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems (20 citations)
  • Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN (20 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Xiaoli Li mainly focuses on Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Electroencephalography. Xiaoli Li has included themes like Task and Natural language processing in his Artificial intelligence study. His Feature learning study, which is part of a larger body of work in Machine learning, is frequently linked to Heterogeneous network, bridging the gap between disciplines.

In Pattern recognition, he works on issues like Series, which are connected to Coherence, Motor control, Biological system and Oscillation. His Electroencephalography research is included under the broader classification of Neuroscience. His Neuroscience research is multidisciplinary, incorporating perspectives in Non-negative matrix factorization and Mutual information.

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

Deep convolutional neural networks on multichannel time series for human activity recognition

Jian Bo Yang;Minh Nhut Nguyen;Phyo Phyo San;Xiao Li Li.
international conference on artificial intelligence (2015)

864 Citations

Building text classifiers using positive and unlabeled examples

B. Liu;Y. Dai;X. Li;W.S. Lee.
international conference on data mining (2003)

686 Citations

Partially Supervised Classification of Text Documents

Bing Liu;Wee Sun Lee;Philip S. Yu;Xiaoli Li.
international conference on machine learning (2002)

585 Citations

A brief review: acoustic emission method for tool wear monitoring during turning

Xiaoli Li.
International Journal of Machine Tools & Manufacture (2002)

576 Citations

Eliminating noisy information in Web pages for data mining

Lan Yi;Bing Liu;Xiaoli Li.
knowledge discovery and data mining (2003)

554 Citations

Learning to classify texts using positive and unlabeled data

Xiaoli Li;Bing Liu.
international joint conference on artificial intelligence (2003)

490 Citations

Computational approaches for detecting protein complexes from protein interaction networks: a survey

Xiaoli Li;Min Wu;Chee-Keong Kwoh;See-Kiong Ng.
BMC Genomics (2010)

362 Citations

A core-attachment based method to detect protein complexes in PPI networks.

Min Wu;Xiaoli Li;Chee Keong Kwoh;See-Kiong Ng.
BMC Bioinformatics (2009)

355 Citations

Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

Giduthuri Sateesh Babu;Peilin Zhao;Xiao-Li Li.
database systems for advanced applications (2016)

354 Citations

Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

Xutao Li;Gao Cong;Xiao-Li Li;Tuan-Anh Nguyen Pham.
international acm sigir conference on research and development in information retrieval (2015)

279 Citations

Best Scientists Citing Xiaoli Li

Yong He

Yong He

Zhejiang University

Publications: 132

Da-Wen Sun

Da-Wen Sun

National University of Ireland

Publications: 61

Jianxin Wang

Jianxin Wang

Central South University

Publications: 50

Fang-Xiang Wu

Fang-Xiang Wu

University of Saskatchewan

Publications: 50

Min Li

Min Li

Central South University

Publications: 40

Yi Pan

Yi Pan

Shenzhen Institutes of Advanced Technology

Publications: 38

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 35

Di Wu

Di Wu

Northeast Agricultural University

Publications: 30

Osvaldo A. Rosso

Osvaldo A. Rosso

National Scientific and Technical Research Council

Publications: 28

Xiaohua Hu

Xiaohua Hu

Drexel University

Publications: 25

Yuefeng Li

Yuefeng Li

Queensland University of Technology

Publications: 23

Luciano Zunino

Luciano Zunino

National University of La Plata

Publications: 21

Lizhe Wang

Lizhe Wang

China University of Geosciences

Publications: 20

Jiawei Han

Jiawei Han

University of Illinois at Urbana-Champaign

Publications: 19

Quansheng Chen

Quansheng Chen

Jiangsu University

Publications: 19

Ruqiang Yan

Ruqiang Yan

Xi'an Jiaotong University

Publications: 19

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

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