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.
Incremental Induction of Decision Trees
1989500 citationsPaul E. UtgoffMachine Learningprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Paul E. Utgoff
Since
Specialization
Citations
This map shows the geographic impact of Paul E. Utgoff'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 Paul E. Utgoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul E. Utgoff more than expected).
This network shows the impact of papers produced by Paul E. Utgoff. 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 Paul E. Utgoff. The network helps show where Paul E. Utgoff may publish in the future.
Co-authorship network of co-authors of Paul E. Utgoff
This figure shows the co-authorship network connecting the top 25 collaborators of Paul E. Utgoff.
A scholar is included among the top collaborators of Paul E. Utgoff 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 Paul E. Utgoff. Paul E. Utgoff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Utgoff, Paul E., et al.. (2006). Detecting Motives and Recurring Patterns in PolyphonicMusic. The Journal of the Abraham Lincoln Association. 2006.5 indexed citations
Utgoff, Paul E., et al.. (2002). Randomized Variable Elimination. Journal of Machine Learning Research. 5. 1331–1362.7 indexed citations
5.
Utgoff, Paul E.. (2001). Feature construction for game playing. Nova Science Publishers, Inc. eBooks. 131–152.10 indexed citations
6.
Utgoff, Paul E., David Jensen, & Victor Lesser. (2000). Inferring Task Structure From Data.2 indexed citations
7.
Utgoff, Paul E., et al.. (1999). Approximation Via Value Unification. International Conference on Machine Learning. 425–432.3 indexed citations
8.
Precup, Doina & Paul E. Utgoff. (1998). Classification Using Phi-Machines and Constructive Function Approximation. International Conference on Machine Learning. 439–444.2 indexed citations
9.
Moss, J. Eliot B., Paul E. Utgoff, John Cavazos, et al.. (1997). Learning to Schedule Straight-Line Code. Neural Information Processing Systems. 10. 929–935.42 indexed citations
10.
Brodley, Carla E. & Paul E. Utgoff. (1995). Multivariate decision trees. Machine Learning. 19(1). 45–77.165 indexed citations
11.
Utgoff, Paul E.. (1994). An Improved Algorithm for Incremental Induction.2 indexed citations
12.
Utgoff, Paul E.. (1994). Algebraic Reasoning about Reactions: Discovery of Conserved Properties in Particle Physics.
13.
Brodley, Carla E. & Paul E. Utgoff. (1992). Multivariate Versus Univariate Decision Trees.32 indexed citations
14.
Utgoff, Paul E., et al.. (1991). Two kinds of training information for evaluation function learning. ScholarWorks@UMassAmherst (University of Massachusetts Amherst). 596–600.40 indexed citations
15.
Callan, James P. & Paul E. Utgoff. (1991). Constructive induction on domain information. National Conference on Artificial Intelligence. 614–619.6 indexed citations
Connell, Margaret E. & Paul E. Utgoff. (1987). Learning to control a dynamic physical system. National Conference on Artificial Intelligence. 456–460.19 indexed citations
18.
Utgoff, Paul E.. (1983). Adjusting bias in concept learning. International Joint Conference on Artificial Intelligence. 447–449.19 indexed citations
19.
Utgoff, Paul E. & Tom M. Mitchell. (1982). Acquisition of appropriate bias for inductive concept learning. National Conference on Artificial Intelligence. 414–417.34 indexed citations
20.
Mitchell, Tom M., et al.. (1981). Learning Problem-Solving Heuristics Through Practice.. International Joint Conference on Artificial Intelligence. 127–134.40 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.