David Martínez‐Rego

27 papers receiving 910 citations

Hit Papers

Cryptocurrency trading: a comprehensive survey2022202620232024202250100150200

Peers

David Martínez‐Rego
Comparison fields: 5 of 124
  • Artificial Intelligence 345
  • Information Systems 220
  • Control and Systems Engineering 155
  • Economics and Econometrics 120
  • Computer Vision and Pattern Recognition 109
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Citations per year

Countries citing papers authored by David Martínez‐Rego

Since Specialization
Citations

This map shows the geographic impact of David Martínez‐Rego'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 David Martínez‐Rego with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Martínez‐Rego more than expected).

Fields of papers citing papers by David Martínez‐Rego

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Martínez‐Rego. 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 David Martínez‐Rego. The network helps show where David Martínez‐Rego may publish in the future.

Co-authorship network of co-authors of David Martínez‐Rego

This figure shows the co-authorship network connecting the top 25 collaborators of David Martínez‐Rego. A scholar is included among the top collaborators of David Martínez‐Rego based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Martínez‐Rego. David Martínez‐Rego is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 3
3 11
4 18
5 13
6 59
7
Scalable approximate k-NN Graph construction based on Locality Sensitive Hashing.
1
8
Algorithmic challenges in big data analytics.
4
9 61
10
A fast learning algorithm for high dimensional problems: an application to microarrays.
1
11 11
12 14
13 111
14 5
15 12
16
One-class classifier based on extreme value statistics.
1
17 143
18 17
19 14
20
A Method for Time Series Prediction using a Combination of Linear Models
10

About David Martínez‐Rego

David Martínez‐Rego is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 28 papers that have together received 940 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (6 papers), Neural Networks and Applications (6 papers) and Fault Detection and Control Systems (5 papers). The work is most often cited by research in Artificial Intelligence (345 citations), Information Systems (220 citations) and Management Science and Operations Research (88 citations). David Martínez‐Rego has collaborated with scholars based in Spain, United Kingdom and United States. Frequent co-authors include Amparo Alonso‐Betanzos, Óscar Fontenla-Romero, Sergio Ramírez‐Gallego, Verónica Bolón‐Canedo, José M. Benítez, Francisco Herrera, Leslie Kanthan, Michail Basios, Fan Wu and Fan Fang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Expert Systems with Applications and Pattern Recognition.

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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