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.
Instance-Based Learning Algorithms
19912.8k citationsDavid W. Aha, Dennis Kibler et al.profile →
Instance-based learning algorithms
19912.3k citationsDavid W. Aha, Dennis Kibler et al.profile →
Citations per year, relative to Dennis Kibler Dennis Kibler (= 1×)
peers
Marc K. Albert
Countries citing papers authored by Dennis Kibler
Since
Specialization
Citations
This map shows the geographic impact of Dennis Kibler'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 Dennis Kibler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dennis Kibler more than expected).
This network shows the impact of papers produced by Dennis Kibler. 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 Dennis Kibler. The network helps show where Dennis Kibler may publish in the future.
Co-authorship network of co-authors of Dennis Kibler
This figure shows the co-authorship network connecting the top 25 collaborators of Dennis Kibler.
A scholar is included among the top collaborators of Dennis Kibler 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 Dennis Kibler. Dennis Kibler is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hu, Yuh‐Jyh, Suzanne Sandmeyer, & Dennis Kibler. (1999). Detecting Motifs from Sequences. International Conference on Machine Learning. 181–190.5 indexed citations
3.
Kibler, Dennis, et al.. (1997). Learning Symbolic Prototypes. International Conference on Machine Learning. 75–82.5 indexed citations
4.
Kibler, Dennis, et al.. (1997). Symbolic nearest mean classifiers. National Conference on Artificial Intelligence. 82–87.12 indexed citations
5.
Hu, Yuh‐Jyh & Dennis Kibler. (1996). Generation of attributes for learning algorithms. National Conference on Artificial Intelligence. 806–811.27 indexed citations
6.
Kibler, Dennis, et al.. (1991). SteppingStone: an empirical and analytical evaluation. National Conference on Artificial Intelligence. 527–532.6 indexed citations
7.
Aha, David W. & Dennis Kibler. (1990). A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations. eScholarship (California Digital Library).57 indexed citations
8.
Hall, Rogers & Dennis Kibler. (1990). Making mathematics on paper : constructing representations of stories about related linear functions. eScholarship (California Digital Library).12 indexed citations
9.
Aha, David W. & Dennis Kibler. (1989). Noise-tolerant instance-based learning algorithms. International Joint Conference on Artificial Intelligence. 794–799.71 indexed citations
10.
Kibler, Dennis, et al.. (1989). Learning subgoal sequences for planning. International Joint Conference on Artificial Intelligence. 609–614.10 indexed citations
11.
Kibler, Dennis & John S. Conery. (1985). Parallelism in AI programs. International Joint Conference on Artificial Intelligence. 53–56.8 indexed citations
Porter, Bruce & Dennis Kibler. (1985). A comparison of analytic and experimental goal regression for machine learning. International Joint Conference on Artificial Intelligence. 555–559.8 indexed citations
Porter, Bruce & Dennis Kibler. (1984). Learning operator transformations. National Conference on Artificial Intelligence. 278–282.8 indexed citations
16.
Conery, John S. & Dennis Kibler. (1983). AND parallelism in logic programs. International Joint Conference on Artificial Intelligence. 539–543.14 indexed citations
17.
Kibler, Dennis & Bruce Porter. (1983). Episodic learning. National Conference on Artificial Intelligence. 191–196.5 indexed citations
18.
Kibler, Dennis & Bruce Porter. (1983). Perturbation: a means for guiding generalization. International Joint Conference on Artificial Intelligence. 415–418.8 indexed citations
19.
Kibler, Dennis & Paul Morris. (1981). Don't be stupid. International Joint Conference on Artificial Intelligence. 345–347.20 indexed citations
20.
Conery, John S., Paul Morris, & Dennis Kibler. (1981). Efficient Logic Programs: A Research Proposal. eScholarship (California Digital Library).3 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.