Tomáš Horváth

39 papers receiving 507 citations

Peers

Tomáš Horváth
Comparison fields: 5 of 110
  • Artificial Intelligence 271
  • Information Systems 173
  • Computer Science Applications 120
  • Computer Vision and Pattern Recognition 75
  • Computer Networks and Communications 41
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Citations per field
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Citations per year

Countries citing papers authored by Tomáš Horváth

Since Specialization
Citations

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

Fields of papers citing papers by Tomáš Horváth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomáš Horváth

This figure shows the co-authorship network connecting the top 25 collaborators of Tomáš Horváth. A scholar is included among the top collaborators of Tomáš Horváth 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 Tomáš Horváth. Tomáš Horváth 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 20
2 1
3 9
4 39
5 6
6 19
7
Abstractive Text Summarization using Transfer Learning.
15
8
Optimization in Federated Learning.
5
9
Efficient Load Profiling and Forecasting in Large Electric Power Systems.
0
10
Evolutionary Federated Learning on EEG-data.
6
11
Real-time Monitoring of Hungarian Highway Traffic from Cell Phone Network Data.
2
12 23
13
Comparing Performance of Formal Concept Analysis and Closed Frequent Itemset Mining Algorithms on Real Data
7
14
Using factorization machines for student modeling.
16
15
A Model of User Preference Learning for Content-Based Recommender Systems
13
16
Factorization techniques for student performance classification and ranking.
2
17
Mining Concepts from Incomplete Datasets Utilizing Matrix Factorization.
1
18
User Preference Web Search -- Experiments with a System Connecting Web and User
5
19
Uncertainty issues in automating process connecting web and user
4
20
An ILP model for a monotone graded classification problem
4

About Tomáš Horváth

Tomáš Horváth is a scholar working on Computational Mathematics, Computer Science Applications and Signal Processing, having authored 40 papers that have together received 538 indexed citations. Recurring topics across this work include Data Management and Algorithms (7 papers), Online Learning and Analytics (6 papers) and Intelligent Tutoring Systems and Adaptive Learning (6 papers). The work is most often cited by research in Computer Science Applications (120 citations), Computational Mathematics (11 citations) and Artificial Intelligence (271 citations). Tomáš Horváth has collaborated with scholars based in Slovakia, Hungary and Germany. Frequent co-authors include André C. P. L. F. de Carvalho, Lars Schmidt-Thieme, Nguyen Thai-Nghe, Lucas Drumond, Rafael Gomes Mantovani, Ricardo Cerri, Joaquin Vanschoren, Ladislav Peška, Peter Vojtáš and Αλέξανδρος Νανόπουλος. Their work appears in journals such as Expert Systems with Applications, Neurocomputing and Applied Soft Computing.

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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