Paulo Lisböa
About
In The Last Decade
Paulo Lisböa
172 papers receiving 3.4k citations
Peers
Comparison fields: 5 of 196
- Artificial Intelligence 1.1k
- Molecular Biology 396
- Orthopedics and Sports Medicine 337
- Biomedical Engineering 309
- Computer Vision and Pattern Recognition 289
Countries citing papers authored by Paulo Lisböa
This map shows the geographic impact of Paulo Lisböa'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 Paulo Lisböa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paulo Lisböa more than expected).
Fields of papers citing papers by Paulo Lisböa
This network shows the impact of papers produced by Paulo Lisböa. 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 Paulo Lisböa. The network helps show where Paulo Lisböa may publish in the future.
Co-authorship network of co-authors of Paulo Lisböa
This figure shows the co-authorship network connecting the top 25 collaborators of Paulo Lisböa. A scholar is included among the top collaborators of Paulo Lisböa 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 Paulo Lisböa. Paulo Lisböa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Societal issues in machine learning: when learning from data is not enough | 1 |
| 3 | 29 | |
| 4 | 14 | |
| 5 | PREDICTING GROUND REACTION FORCES FROM TRUNK KINEMATICS: A MASS-SPRING-DAMPER MODEL APPROACH | 2 |
| 6 | Physics and machine learning: Emerging paradigms | 2 |
| 7 | Performance assessment of quantum clustering in non-spherical data distributions. | 2 |
| 8 | 70 | |
| 9 | Research directions in interpretable machine learning models | 12 |
| 10 | Constructing similarity networks using the Fisher information metric. | 2 |
| 11 | The role of Fisher information in primary data space for neighbourhood mapping | 5 |
| 12 | Computational Intelligence in biomedicine: Some contributions. | 4 |
| 13 | Machine Learning in cancer research: implications for personalised medicine | 9 |
| 14 | 11 | |
| 15 | Learning what is important: feature selection and rule extraction in a virtual course | 20 |
| 16 | Handling outliers and missing data in brain tumour clinical assessment using t-GTM. | 3 |
| 17 | Functional topographic mapping for robust handling of outliers in brain tumour data. | 1 |
| 18 | Inter-trial phase synchronisation in the ERP Delta band accounts for differences in oddball P300 | 1 |
| 19 | 13 | |
| 20 | The generative topographic mapping as a principal model for data visualization and market segmentation: an electronic commerce case. | 2 |
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.