Amaury Lendasse
About
In The Last Decade
Amaury Lendasse
199 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Artificial Intelligence 2.9k
- Computer Vision and Pattern Recognition 991
- Electrical and Electronic Engineering 891
- Management Science and Operations Research 463
- Signal Processing 426
Countries citing papers authored by Amaury Lendasse
This map shows the geographic impact of Amaury Lendasse'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 Amaury Lendasse with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amaury Lendasse more than expected).
Fields of papers citing papers by Amaury Lendasse
This network shows the impact of papers produced by Amaury Lendasse. 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 Amaury Lendasse. The network helps show where Amaury Lendasse may publish in the future.
Co-authorship network of co-authors of Amaury Lendasse
This figure shows the co-authorship network connecting the top 25 collaborators of Amaury Lendasse. A scholar is included among the top collaborators of Amaury Lendasse 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 Amaury Lendasse. Amaury Lendasse 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 | 33 | |
| 3 | Underwater obstacle detection via relative total variation and joint guided filtering for autonomous underwater vehicles | 1 |
| 4 | Deep Spectral Descriptors: Learning the point-wise correspondence metric via Siamese deep neural networks. | 5 |
| 5 | Online fish tracking with portable smart device for ocean observatory network | 2 |
| 6 | Proceedings of ELM-2015 Volume 2: Theory, Algorithms and Applications (II) | 2 |
| 7 | 6 | |
| 8 | Relevance learning for time series inspection | 1 |
| 9 | Ensembles of Locally Linear Models: Application to Bankruptcy Prediction. | 1 |
| 10 | Machine Learning Techniques based on Random Projections. | 11 |
| 11 | X-SOM and L-SOM: a Nested Approach for Missing Value Imputation | 3 |
| 12 | A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM | 48 |
| 13 | Using the Delta Test for Variable Selection | 29 |
| 14 | Determination of the Mahalanobis matrix using nonparametric noise estimations | 4 |
| 15 | Mutual Information and Gamma Test for Input Selection | 12 |
| 16 | Self-organizing Feature Maps for the Classificaion of Investment Funds | 2 |
| 17 | Long-term time series forecasting using self-organizing maps : the double vector quantization method | 2 |
| 18 | Input data reduction for the prediction of financial time series | 11 |
| 19 | Extraction of intrinsic dimension using CCA-Application to blind sources separation. | 2 |
| 20 | Forecasting Time-Series by Kohonen Classification | 17 |
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.