Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning and generalization characteristics of the random vector functional-link net
1994836 citationsYoh‐Han Pao, D.J. Šobajić et al.Neurocomputingprofile →
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
1995725 citationsB. Igelnik, Yoh‐Han PaoIEEE Transactions on Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
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).
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.
Igelnik, B. & Yoh‐Han Pao. (1995). Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Transactions on Neural Networks. 6(6). 1320–1329.725 indexed citations breakdown →
Pao, Yoh‐Han & D.J. Šobajić. (1991). 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 ). 111(7). 690–697.
11.
Pao, Yoh‐Han, et al.. (1991). A COMPUTER-BASED ADAPTIVE ASSOCIATIVE MEMORY IN SUPPORT OF DESIGN AND PLANNING.2 indexed citations
Pao, Yoh‐Han & George W. Ernst. (1982). Tutorial, context-directed pattern recognition and machine intelligence techniques for information processing. Medical Entomology and Zoology.1 indexed citations
17.
Pao, Yoh‐Han, et al.. (1976). Application of a pulsed dye laser to optoacoustic detection of NO 2 (A). Journal of the Optical Society of America A. 66. 1072.1 indexed citations
18.
Pao, Yoh‐Han, et al.. (1973). A VIDEO BANDWIDTH HE-NE LASER COMMUNICATION SYSTEM. UA Campus Repository (The University of Arizona).1 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.