Robert J. Durrant

1.1k citations
22 papers · 731 · h-index 12

Impact in

Papers in

Robert J. Durrant

22 papers receiving 709 citations

Peers

Robert J. Durrant
Comparison fields: 5 of 104
  • Microbiology 28
  • Endocrinology, Diabetes and Metabolism 307
  • Artificial Intelligence 157
  • Computer Vision and Pattern Recognition 82
  • Clinical Biochemistry 22
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H. El Gamal United States
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Andrew J. Sinclair United States
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Citations per year

Countries citing papers authored by Robert J. Durrant

Since Specialization
Citations

This map shows the geographic impact of Robert J. Durrant's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Robert J. Durrant with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert J. Durrant more than expected).

Fields of papers citing papers by Robert J. Durrant

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert J. Durrant. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Robert J. Durrant. The network helps show where Robert J. Durrant may publish in the future.

Co-authors

The 25 scholars most cited alongside Robert J. Durrant, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert J. Durrant Line = papers co-authored together Robert J. Durrant links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005385
2 200949
3 201541
4 201532
5 201032
6 201930
7 201427
8 201926
9 200825
10 202118
11
Sharp Generalization Error Bounds for Randomly-projected Classifiers
201314
12 201313
13
Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions
201310
14 20176
15
Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space
20126
16 20165
17 20114
18
A diversity-aware model for majority vote ensemble accuracy
20202
19 20202
20 20172

About Robert J. Durrant

Robert J. Durrant is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Clinical Biochemistry and Infectious Diseases, having authored 22 papers that have together received 731 indexed citations. Recurring topics across this work include Face and Expression Recognition (7 papers), Sparse and Compressive Sensing Techniques (5 papers), Bacterial Identification and Susceptibility Testing (4 papers), Statistical Methods and Inference (3 papers), Metaheuristic Optimization Algorithms Research (3 papers), Bayesian Methods and Mixture Models (3 papers), Evolutionary Algorithms and Applications (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Microbiology (28 citations), Endocrinology, Diabetes and Metabolism (307 citations), Artificial Intelligence (157 citations), Computer Vision and Pattern Recognition (82 citations) and Clinical Biochemistry (22 citations). Robert J. Durrant has collaborated with scholars based in United Kingdom, New Zealand and United States. Frequent co-authors include Ata Kabán, Margaret Band, Louise A. Donnelly, Andrew D. Morris, Graham Leese, Peter T. Donnan, John Ellis, Brian M. Frier, Jakramate Bootkrajang and Cathy A. Petti. Their work appears in journals such as Diagnostic Microbiology and Infectious Disease, Journal of Clinical Microbiology, Diabetic Medicine, PLoS ONE and Knowledge and Information Systems.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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