Thomas Petsche

403 total citations
10 papers, 279 citations indexed

About

Thomas Petsche is a scholar working on Artificial Intelligence, Control and Systems Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Thomas Petsche has authored 10 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Control and Systems Engineering and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Thomas Petsche's work include Neural Networks and Applications (3 papers), Machine Learning and Algorithms (3 papers) and Algorithms and Data Compression (3 papers). Thomas Petsche is often cited by papers focused on Neural Networks and Applications (3 papers), Machine Learning and Algorithms (3 papers) and Algorithms and Data Compression (3 papers). Thomas Petsche collaborates with scholars based in United States and Germany. Thomas Petsche's co-authors include Bernhard Schölkopf, Anthony Kuh, Ronald L. Rivest, B. Dickinson, Stephen José Hanson, Gary M. Kuhn, N.I. Santoso, Christian J. Darken, Bradley W. Dickinson and Russell Greiner and has published in prestigious journals such as IEEE Transactions on Neural Networks, neural information processing systems and Neural Information Processing Systems.

In The Last Decade

Thomas Petsche

10 papers receiving 258 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Thomas Petsche United States	6	194	103	46	30	23	10	279
Fred W. Smith United States	9	137 0.7×	119 1.2×	66 1.4×	20 0.7×	18 0.8×	35	337
J.H. Kim South Korea	5	247 1.3×	168 1.6×	44 1.0×	31 1.0×	21 0.9×	6	394
Puyin Liu China	11	205 1.1×	42 0.4×	69 1.5×	14 0.5×	21 0.9×	19	306
Khalid Benabdeslem France	8	189 1.0×	114 1.1×	23 0.5×	56 1.9×	15 0.7×	28	295
Chandan Gautam India	10	199 1.0×	91 0.9×	30 0.7×	22 0.7×	16 0.7×	19	263
Pavan Kumar Mallapragada United States	5	250 1.3×	215 2.1×	21 0.5×	24 0.8×	21 0.9×	7	381
Mathias Berglund Finland	5	137 0.7×	80 0.8×	16 0.3×	32 1.1×	17 0.7×	5	247
Yifan Fu Australia	7	222 1.1×	92 0.9×	13 0.3×	33 1.1×	13 0.6×	10	338
Jihwan Jeong South Korea	5	280 1.4×	163 1.6×	12 0.3×	23 0.8×	28 1.2×	8	356

Countries citing papers authored by Thomas Petsche

Since Specialization

Citations

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

Fields of papers citing papers by Thomas Petsche

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Petsche

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Kuh, Anthony & Thomas Petsche. (2005). Learning Time Varying Concepts with Applications to Pattern Recognition Problems. 2. 971–971. 3 indexed citations

2.

Greiner, Russell, Thomas Petsche, & Stephen José Hanson. (1997). Computational learning theory and natural learning systems: Volume IV: making learning systems practical. Conference on Learning Theory. 407–407. 4 indexed citations

3.

Schölkopf, Bernhard, et al.. (1997). Improving the accuracy and speed of support vector learning machines. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 375–381. 156 indexed citations

4.

Greiner, Russell, Thomas Petsche, & Stephen José Hanson. (1997). N-Learners Problem: System of PAC Learners. 189–210. 1 indexed citations

5.

Greiner, Russell, Thomas Petsche, & Stephen José Hanson. (1997). Initializing Neural Networks Using Decision Trees. 3–15. 2 indexed citations

6.

Petsche, Thomas, et al.. (1995). A Neural Network Autoassociator for Induction Motor Failure Prediction. Neural Information Processing Systems. 8. 924–930. 32 indexed citations

7.

Kuh, Anthony, Thomas Petsche, & Ronald L. Rivest. (1991). Incrementally Learning Time-varying Half-planes. neural information processing systems. 4. 920–927. 7 indexed citations

8.

Kuh, Anthony, Thomas Petsche, & Ronald L. Rivest. (1990). Learning Time-varying Concepts. neural information processing systems. 3. 183–189. 47 indexed citations

9.

Petsche, Thomas & B. Dickinson. (1990). Trellis codes, receptive fields, and fault tolerant, self-repairing neural networks. IEEE Transactions on Neural Networks. 1(2). 154–166. 22 indexed citations

10.

Petsche, Thomas & Bradley W. Dickinson. (1987). A Trellis-Structured Neural Network. Neural Information Processing Systems. 592–601. 5 indexed citations

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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