Jack Sklansky

8.2k citations
124 papers · 5.0k · 2 hit papers · h-index 31

Impact in

Papers in

Jack Sklansky

117 papers receiving 4.6k citations

Jack Sklansky's Hit Papers

Comparison of algorithms that select features for pattern classifiers 2000 · 613 citations
6130+12+24Years since publication200400600

Peers

Jack Sklansky
Comparison fields: 5 of 173
  • Computer Vision and Pattern Recognition 2.4k
  • Computer Graphics and Computer-Aided Design 254
  • Artificial Intelligence 1.6k
  • Media Technology 342
  • Signal Processing 387
Replace Anand Rangarajan with:
Anand Rangarajan United States
King‐Sun Fu United States
Roger Boyle United Kingdom
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Alessandro Verri Italy
Edwin R. Hancock United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Jack Sklansky

Since Specialization
Citations

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

Fields of papers citing papers by Jack Sklansky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jack Sklansky, 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 Jack Sklansky Line = papers co-authored together Jack Sklansky links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 124 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Comparison of algorithms that select features for pattern classifiers
Hit paper breakdown →
2000613
2
A note on genetic algorithms for large-scale feature selection
Hit paper breakdown →
1989579
3 1975418
4 1960389
5 1988259
6 1980227
7 1978167
8 1982157
9 1972134
10 1981113
11 1980108
12 1983107
13 198896
14 197292
15 197081
16 198175
17 197764
18 198062
19 198757
20 199053

About Jack Sklansky

Jack Sklansky is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Control and Systems Engineering and Biophysics, having authored 124 papers that have together received 5.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (20 papers), Digital Image Processing Techniques (20 papers), Medical Image Segmentation Techniques (20 papers), Machine Learning and Data Classification (11 papers), Cell Image Analysis Techniques (10 papers), Medical Imaging Techniques and Applications (9 papers), Face and Expression Recognition (8 papers) and Advanced Vision and Imaging (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Computer Graphics and Computer-Aided Design (254 citations), Artificial Intelligence (1.6k citations), Media Technology (342 citations) and Signal Processing (387 citations). Jack Sklansky has collaborated with scholars based in United States, Japan and Israel. Frequent co-authors include Wojciech Siedlecki, Mineichi Kudo, Dana H. Ballard, Víctor M. González, M. Hassner, Young-Tae Park, Jonathan M. Tobis, Kenji Kitamura, Harry Wechsler and Gene H. Hostetter. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Medical Imaging, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Computers and Pattern Recognition 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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