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.
Comparison of algorithms that select features for pattern classifiers
2000613 citationsMineichi Kudo, Jack Sklanskyprofile →
A note on genetic algorithms for large-scale feature selection
1989579 citationsWojciech Siedlecki, Jack Sklanskyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
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).
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-authorship network of co-authors of Jack Sklansky
This figure shows the co-authorship network connecting the top 25 collaborators of Jack Sklansky.
A scholar is included among the top collaborators of Jack Sklansky 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 Jack Sklansky. Jack Sklansky is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kudo, Mineichi & Jack Sklansky. (1998). A COMPARATIVE EVALUATION OF MEDIUM-AND LARGE-SCALE FEATURE SELECTORS FOR PATTERN CLASSIFIERS. Kybernetika. 34(4). 429–434.20 indexed citations
5.
Sklansky, Jack, et al.. (1998). A visual neural classifier. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 28(4). 620–625.8 indexed citations
Siedlecki, Wojciech & Jack Sklansky. (1989). Constrained Genetic Optimization via Dynarnic Reward-Penalty Balancing and Its Use in Pattern Recognition. international conference on Genetic algorithms. 141–150.50 indexed citations
Bisconte, J.-C. & Jack Sklansky. (1982). Biomedical images and computers : selected papers presented at the United States-France Seminar on Biomedical Image Processing, St. Pierre de Chartreuse, France, May 27-31, 1980. Springer eBooks.2 indexed citations
16.
Sklansky, Jack, et al.. (1971). A Parallel Mechanism for Recognizing Silhouettes.. IFIP Congress. 224–228.1 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.