Fabian Moerchen

905 citations
26 papers · 590 · h-index 10

Impact in

Papers in

    • Time Series Analysis and Forecasting 7
    • Music and Audio Processing 5
    • Data Management and Algorithms 4
    • Data Mining Algorithms and Applications 11

Fabian Moerchen

25 papers receiving 552 citations

Peers

Fabian Moerchen
Comparison fields: 5 of 85
  • Signal Processing 222
  • Computational Mathematics 6
  • Medical Laboratory Technology 14
  • Artificial Intelligence 290
  • Information Systems 194
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Chris Price United Kingdom
A.L.P. Chen Taiwan
Ali Miri Canada
Rahil Hosseini Iran
Anastasios Gounaris Greece
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Cungen Cao China
Mark Nicholson United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Fabian Moerchen

Since Specialization
Citations

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

Fields of papers citing papers by Fabian Moerchen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Fabian Moerchen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Fabian Moerchen Line = papers co-authored together Fabian Moerchen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014122
2 2012110
3
Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach
201277
4 200663
5 201048
6 201533
7 201019
8 200618
9 201014
10 201013
11 20149
12
Emerging trend prediction in biomedical literature.
20088
13
Any-time clustering of high frequency news streams
20078
14 20108
15 20107
16 20206
17 20074
18 20094
19 20094
20 20124

About Fabian Moerchen

Fabian Moerchen is a scholar working on Signal Processing, Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 26 papers that have together received 590 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (11 papers), Time Series Analysis and Forecasting (7 papers), Rough Sets and Fuzzy Logic (6 papers), Music and Audio Processing (5 papers), Data Management and Algorithms (4 papers), Advanced Database Systems and Queries (4 papers), Video Analysis and Summarization (3 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Signal Processing (222 citations), Computational Mathematics (6 citations), Medical Laboratory Technology (14 citations), Artificial Intelligence (290 citations) and Information Systems (194 citations). Fabian Moerchen has collaborated with scholars based in United States, Germany and Hong Kong. Frequent co-authors include Dmitriy Fradkin, Zhuang Wang, James H. Harrison, Iyad Batal, Miloš Hauskrecht, Liang Lan, Kai Zhang, Alfred Ultsch, Hassan Malik and Ingo Mierswa. Their work appears in journals such as Knowledge and Information Systems, Data Mining and Knowledge Discovery, ACM Transactions on Database Systems, International Journal of Semantic Computing and arXiv (Cornell University).

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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