Viktor Losing

856 citations
18 papers · 620 · h-index 9

Impact in

    • Data Stream Mining Techniques
    • Anomaly Detection Techniques and Applications
    • Machine Learning and Data Classification
    • Machine Learning and ELM
    • Imbalanced Data Classification Techniques
    • Time Series Analysis and Forecasting

Papers in

Viktor Losing

17 papers receiving 608 citations

Peers

Viktor Losing
Comparison fields: 5 of 83
  • Artificial Intelligence 482
  • Signal Processing 98
  • Management Science and Operations Research 59
  • Computer Vision and Pattern Recognition 86
  • Computer Networks and Communications 95
Replace M. Elif Karslıgil with:
M. Elif Karslıgil Türkiye
Haoxi Zhang China
Shilpa Rani India
Junzhao Du China
Bartłomiej Śnieżyński Poland
Charles Shelton United States
Véronique Cherfaoui France
Emad-ul-Haq Qazi Saudi Arabia
Ghazaleh Khodabandelou France
Viktor Losing relative to M. Elif Karslıgil Türkiye M. Elif Karslıgil's profile →
Citations per field
00.5×1.5×2.0×
M. Elif Karslıgil · 1×
Citations per year

Countries citing papers authored by Viktor Losing

Since Specialization
Citations

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

Fields of papers citing papers by Viktor Losing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Viktor Losing, 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 Viktor Losing Line = papers co-authored together Viktor Losing links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2017268
2 2016163
3 201736
4 201534
5 202226
6 201718
7
Choosing the Best Algorithm for an Incremental On-line Learning Task
201617
8 201416
9 20188
10 20178
11 20207
12 20197
13 20185
14 20203
15 20212
16 20201
17 20221
18 20220

About Viktor Losing

Viktor Losing is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computer Networks and Communications and Biomedical Engineering, having authored 18 papers that have together received 620 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (9 papers), Machine Learning and Data Classification (9 papers), Anomaly Detection Techniques and Applications (5 papers), Advanced Bandit Algorithms Research (4 papers), Topic Modeling (2 papers), Human Pose and Action Recognition (2 papers), Image Processing Techniques and Applications (1 paper) and Vehicle emissions and performance (1 paper). The work is most often cited by research in Artificial Intelligence (482 citations), Signal Processing (98 citations), Management Science and Operations Research (59 citations), Computer Vision and Pattern Recognition (86 citations) and Computer Networks and Communications (95 citations). Viktor Losing has collaborated with scholars based in Germany, Japan and France. Frequent co-authors include Barbara Hammer, Heiko Wersing, Martina Hasenjäger, Thies Pfeiffer, Albert Bifet, Emel Demircan, Talel Abdessalem, Lydia Fischer, Jacob Montiel and Jesse Read. Their work appears in journals such as Neurocomputing, Scientific Data, Advanced Robotics, Knowledge and Information Systems and PUB – Publications at Bielefeld University (Bielefeld 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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