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.
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
2016791 citationsSarah Erfani, Sutharshan Rajasegarar et al.Pattern Recognitionprofile →
Survey of network-based defense mechanisms countering the DoS and DDoS problems
2007507 citationsChristopher Leckie, Kotagiri Ramamohanarao et al.profile →
Fuzzy c-Means Algorithms for Very Large Data
2012353 citationsJames C. Bezdek, Christopher Leckie et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Christopher Leckie
Since
Specialization
Citations
This map shows the geographic impact of Christopher Leckie'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 Christopher Leckie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christopher Leckie more than expected).
Fields of papers citing papers by Christopher Leckie
This network shows the impact of papers produced by Christopher Leckie. 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 Christopher Leckie. The network helps show where Christopher Leckie may publish in the future.
Co-authorship network of co-authors of Christopher Leckie
This figure shows the co-authorship network connecting the top 25 collaborators of Christopher Leckie.
A scholar is included among the top collaborators of Christopher Leckie 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 Christopher Leckie. Christopher Leckie is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Erfani, Sarah, Mahsa Baktashmotlagh, Masud Moshtaghi, et al.. (2016). Robust domain generalisation by enforcing distribution invariance. QUT ePrints (Queensland University of Technology). 1455–1461.11 indexed citations
Lim, Kwan Hui, Jeffrey Chan, Christopher Leckie, & Shanika Karunasekera. (2015). Personalized tour recommendation based on user interests and points of interest visit durations. RMIT Research Repository (RMIT University Library).100 indexed citations
11.
Demyanov, Sergey, James Bailey, Kotagiri Ramamohanarao, & Christopher Leckie. (2012). AIC and BIC based approaches for SVM parameter value estimation with RBF kernels. Asian Conference on Machine Learning. 25. 97–112.7 indexed citations
Kan, Andrey, Jeffrey Chan, James Bailey, & Christopher Leckie. (2009). A query based approach for mining evolving graphs. 101. 139–150.7 indexed citations
15.
Bezdek, James C., Richard J. Hathaway, Christopher Leckie, & Kotagiri Ramamohanarao. (2006). Approximate data mining in very large relational data. Australasian Database Conference. 49. 3–13.2 indexed citations
16.
Leckie, Christopher, et al.. (2005). Unsupervised anomaly detection in network intrusion detection using clusters. 38. 333–342.213 indexed citations
17.
Leckie, Christopher, et al.. (2003). Applying Reinforcement Learning to Packet Scheduling in Routers. Innovative Applications of Artificial Intelligence. 79–84.10 indexed citations
18.
Leckie, Christopher & Kotagiri Ramamohanarao. (2002). Learning to Share Distributed Probabilistic Beliefs. International Conference on Machine Learning. 371–378.5 indexed citations
19.
Leckie, Christopher & M. B. Dale. (1997). Locating Faults in Tree-Structured Networks.. International Joint Conference on Artificial Intelligence. 434–439.4 indexed citations
20.
Leckie, Christopher & Ingrid Zukerman. (1993). An Inductive Approach to Learning Search Control Rules for Planning.. International Joint Conference on Artificial Intelligence. 1100–1105.8 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.