Věra Kůrková

64 papers receiving 1.6k citations

Hit Papers

Kolmogorov's theorem and multilayer neural networks19922026200320141992100200300400

Peers

Věra Kůrková
Comparison fields: 5 of 149
  • Artificial Intelligence 937
  • Control and Systems Engineering 340
  • Computer Vision and Pattern Recognition 315
  • Computational Theory and Mathematics 234
  • Statistical and Nonlinear Physics 210
Replace Hao Yu with:
Hao Yu Singapore
Bharath K. Sriperumbudur United States
Debao Chen China
Bernard Delyon France
Hong Chen China
Andrea Caponnetto Italy
Takafumi Kanamori Japan
Martin Anthony United Kingdom
Ingo Steinwart United States
Yi Liu China
Věra Kůrková relative to Hao Yu Singapore Hao Yu's profile →
Citations per field
00.5×1.5×1.8×
Hao Yu · 1×
Citations per year

Countries citing papers authored by Věra Kůrková

Since Specialization
Citations

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

Fields of papers citing papers by Věra Kůrková

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Věra Kůrková. 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 Věra Kůrková. The network helps show where Věra Kůrková may publish in the future.

Co-authorship network of co-authors of Věra Kůrková

This figure shows the co-authorship network connecting the top 25 collaborators of Věra Kůrková. A scholar is included among the top collaborators of Věra Kůrková 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 Věra Kůrková. Věra Kůrková 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
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II
8
2 7
3 17
4
Multivariable Approximation by Convolutional Kernel Networks.
1
5
Limitations of One-Hidden-Layer Perceptron Networks.
1
6 8
7
Surrogate solutions of Fredholm equations by feedforward networks.
2
8 16
9 14
10 33
11 13
12 28
13 14
14 10
15
Dealing with complexity : a neural networks approach
6
16
Approximation of Functions by Neural Networks.
3
17 5
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Approximation of continuous functions by RBF and KBF networks.
2
19
Uniqueness of network parametrization and faster learning
4
20 24

About Věra Kůrková

Věra Kůrková is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 66 papers that have together received 1.7k indexed citations. Recurring topics across this work include Neural Networks and Applications (42 papers), Image and Signal Denoising Methods (14 papers) and Fuzzy Logic and Control Systems (11 papers). The work is most often cited by research in Artificial Intelligence (937 citations), Statistical and Nonlinear Physics (210 citations) and Computer Vision and Pattern Recognition (315 citations). Věra Kůrková has collaborated with scholars based in Czechia, Italy and United States. Frequent co-authors include Marcello Sanguineti, Paul C. Kainen, Barbara Hammer, Lazaros Iliadis, Ilias Maglogiannis, Yannis Manolopoulos, Andrew Vogt, Pavel Karpov, Fabian J. Theis and Igor V. Tetko. Their work appears in journals such as IEEE Transactions on Information Theory, Neural Computation and IEEE Transactions on Neural Networks and Learning Systems.

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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