Michael D. Muhlbaier

480 citations
5 papers · 219 indexed · h-index 5
Topics
Data Stream Mining Techniques (5 papers)Machine Learning and Data Classification (3 papers)Advanced Bandit Algorithms Research (2 papers)
Journals
IEEE Transactions on Neural NetworksProceedings - International Conference on Pattern Recognition
Partner nations
United States

In The Last Decade

Michael D. Muhlbaier

5 papers receiving 215 citations

Peers

Michael D. Muhlbaier
Comparison fields: 5 of 41
  • Artificial Intelligence 196
  • Computer Vision and Pattern Recognition 36
  • Management Science and Operations Research 29
  • Electrical and Electronic Engineering 28
  • Signal Processing 21
Replace Andrea Cossu with:
Andrea Cossu Italy
Konrad Jackowski Poland
Zain Ul Abideen Pakistan
G. Ramesh India
Ürün Doǧan United States
Yulong Chen China
Jagadish S. Kallimani India
Xu Tan China
Ayush K Tarun India
Dileep Kumar Singh India
Michael D. Muhlbaier relative to Andrea Cossu Italy Andrea Cossu's profile →
Citations per field
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Citations per year

Countries citing papers authored by Michael D. Muhlbaier

Since Specialization
Citations

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

Fields of papers citing papers by Michael D. Muhlbaier

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael D. Muhlbaier. 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 Michael D. Muhlbaier. The network helps show where Michael D. Muhlbaier may publish in the future.

Co-authorship network of co-authors of Michael D. Muhlbaier

This figure shows the co-authorship network connecting the top 25 collaborators of Michael D. Muhlbaier. A scholar is included among the top collaborators of Michael D. Muhlbaier 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 Michael D. Muhlbaier. Michael D. Muhlbaier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

About Michael D. Muhlbaier

Michael D. Muhlbaier is a scholar working on Management Science and Operations Research, Artificial Intelligence and Electrical and Electronic Engineering, having authored 5 papers that have together received 219 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (5 papers), Machine Learning and Data Classification (3 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Artificial Intelligence (196 citations), Management Science and Operations Research (29 citations) and Signal Processing (21 citations). Michael D. Muhlbaier has collaborated with scholars based in United States. Frequent co-authors include Robi Polikar and Apostolos Topalis. Their work appears in journals such as IEEE Transactions on Neural Networks and Proceedings - International Conference on Pattern Recognition.

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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