José M. Valls
About
In The Last Decade
José M. Valls
28 papers receiving 281 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 158
- Electrical and Electronic Engineering 102
- Cognitive Neuroscience 37
- Signal Processing 35
- Computer Vision and Pattern Recognition 33
Countries citing papers authored by José M. Valls
This map shows the geographic impact of José M. Valls'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 José M. Valls with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José M. Valls more than expected).
Fields of papers citing papers by José M. Valls
This network shows the impact of papers produced by José M. Valls. 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 José M. Valls. The network helps show where José M. Valls may publish in the future.
Co-authorship network of co-authors of José M. Valls
This figure shows the co-authorship network connecting the top 25 collaborators of José M. Valls. A scholar is included among the top collaborators of José M. Valls 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 José M. Valls. José M. Valls 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 | 12 | |
| 3 | Supervised Data Transformation by Means of Neural Network Hidden Layer [R package nntrf version 0.1.3] | 1 |
| 4 | 21 | |
| 5 | 26 | |
| 6 | 8 | |
| 7 | 16 | |
| 8 | 23 | |
| 9 | 2 | |
| 10 | 4 | |
| 11 | 1 | |
| 12 | EVOLVING GENERALIZED EUCLIDEAN DISTANCES FOR TRAINING RBNN | 5 |
| 13 | A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems | 6 |
| 14 | LRBNN: A Lazy Radial Basis Neural Network model | 4 |
| 15 | 8 | |
| 16 | 3 | |
| 17 | Improving the Generalization Ability of RBNN Using a Selective Strategy Based on the Gaussian Kernel Function. | 3 |
| 18 | 18 | |
| 19 | How the selection of training patterns can improve the generalization capability in Radial Basis Neural Networks | 1 |
| 20 | Optimizing the Number of Learning Cycles in the Design of Radial Basis Neural Networks Using a Multi-Agent System | 1 |
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.