Carlos Domingo
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Computer Networks and Communications
- Co-authors
- Osamu WatanabeRicard GavaldàNina MishraLeonard PittToniann PitassiMarı́a Luisa BonetJaime Ricardo Valenzuela GonzálezPablo García‐Fernández
- Topics
- Machine Learning and Algorithms (5 papers)Machine Learning and Data Classification (3 papers)Algorithms and Data Compression (3 papers)
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsComputer Graphics and Computer-Aided Design
- Partner nations
- JapanSpainUnited States
In The Last Decade
Carlos Domingo
10 papers receiving 231 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 189
- Computational Theory and Mathematics 69
- Computer Vision and Pattern Recognition 58
- Information Systems 32
- Computer Networks and Communications 16
Countries citing papers authored by Carlos Domingo
This map shows the geographic impact of Carlos Domingo'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 Carlos Domingo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Carlos Domingo more than expected).
Fields of papers citing papers by Carlos Domingo
This network shows the impact of papers produced by Carlos Domingo. 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 Carlos Domingo. The network helps show where Carlos Domingo may publish in the future.
Co-authorship network of co-authors of Carlos Domingo
This figure shows the co-authorship network connecting the top 25 collaborators of Carlos Domingo. A scholar is included among the top collaborators of Carlos Domingo 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 Carlos Domingo. Carlos Domingo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Reconocimiento visual móvil: el futuro de la realidad aumentada móvil | 0 |
| 3 | La Web 2.0. Una revolución social y creativa | 3 |
| 4 | Modelos educactivos: del Taylorismo al e-learning | 0 |
| 5 | 22 | |
| 6 | 6 | |
| 7 | 65 | |
| 8 | MadaBoost: A Modification of AdaBoost | 109 |
| 9 | 34 | |
| 10 | 5 | |
| 11 | Learning minor closed graph classes with membership and equivalence queries | 1 |
| 12 | Old and new models of Venezuela | 2 |
About Carlos Domingo
Carlos Domingo is a scholar working on General Social Sciences, Linguistics and Language and Artificial Intelligence, having authored 12 papers that have together received 250 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (5 papers), Machine Learning and Data Classification (3 papers) and Algorithms and Data Compression (3 papers). The work is most often cited by research in Artificial Intelligence (189 citations), Computational Theory and Mathematics (69 citations) and Computer Graphics and Computer-Aided Design (10 citations). Carlos Domingo has collaborated with scholars based in Japan, Spain and United States. Frequent co-authors include Osamu Watanabe, Ricard Gavaldà, Nina Mishra, Leonard Pitt, Toniann Pitassi, Marı́a Luisa Bonet, Jaime Ricardo Valenzuela González, Pablo García‐Fernández, Tomasz Adamek and John Shawe‐Taylor. Their work appears in journals such as Machine Learning, Data Mining and Knowledge Discovery and Information Processing Letters.
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.