Yoh‐Han Pao

105 papers receiving 3.9k citations

Hit Papers

Learning and generalization characteristics of the random...199420262004201519941995250500750

Peers

Yoh‐Han Pao
Comparison fields: 5 of 177
  • Artificial Intelligence 1.8k
  • Electrical and Electronic Engineering 911
  • Control and Systems Engineering 794
  • Computer Vision and Pattern Recognition 582
  • Atomic and Molecular Physics, and Optics 382
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G. Beni United States
Koji Tsuda Japan
Asok Ray United States
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R. Ian Fletcher United States
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John Hertz Denmark
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Govind P. Gupta India
Vladimı́r Kvasnička Slovakia
Yoh‐Han Pao relative to G. Beni United States G. Beni's profile →
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Citations per year

Countries citing papers authored by Yoh‐Han Pao

Since Specialization
Citations

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

Fields of papers citing papers by Yoh‐Han Pao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yoh‐Han Pao. 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 Yoh‐Han Pao. The network helps show where Yoh‐Han Pao may publish in the future.

Co-authorship network of co-authors of Yoh‐Han Pao

This figure shows the co-authorship network connecting the top 25 collaborators of Yoh‐Han Pao. A scholar is included among the top collaborators of Yoh‐Han Pao 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 Yoh‐Han Pao. Yoh‐Han Pao 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 15
2 56
3 87
4 14
5 47
6
Neural net process monitoring and optimal control
3
7 1
8
Stochastic choice of basis functions in adaptive function approximation and the functional-link netbreakdown →
725
9 105
10 32
11 39
12
Current Status of Artificial Neural Network Applications to Power Systems in the United States (電力・エネルギ-分野におけるニュ-ラルネットワ-ク応用 )
0
13
Functional link nets: removing hidden layers
18
14
Applications of Neural-Net Computing.
1
15 1
16 1
17
Tutorial, context-directed pattern recognition and machine intelligence techniques for information processing
1
18
Application of a pulsed dye laser to optoacoustic detection of NO 2 (A)
1
19
A VIDEO BANDWIDTH HE-NE LASER COMMUNICATION SYSTEM
1
20 46

About Yoh‐Han Pao

Yoh‐Han Pao is a scholar working on Artificial Intelligence, Spectroscopy and Control and Systems Engineering, having authored 112 papers that have together received 4.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (38 papers), Fuzzy Logic and Control Systems (16 papers) and Spectroscopy and Laser Applications (11 papers). The work is most often cited by research in Artificial Intelligence (1.8k citations), Control and Systems Engineering (794 citations) and Computer Vision and Pattern Recognition (582 citations). Yoh‐Han Pao has collaborated with scholars based in United States, Serbia and Canada. Frequent co-authors include D.J. Šobajić, B. Igelnik, Gwang-Hoon Park, H. L. Frisch, R. Bersohn, Leon Sterling, Stephen M. Phillips, Randall D. Beer, P. M. Rentzepis and C. L. Philip Chen. Their work appears in journals such as Journal of the American Chemical Society, The Journal of Chemical Physics and Applied Physics Letters.

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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