Philip K. Chan
About
In The Last Decade
Philip K. Chan
163 papers receiving 8.1k citations
Hit Papers
Peers
Comparison fields: 5 of 198
- Artificial Intelligence 4.5k
- Computer Networks and Communications 2.1k
- Signal Processing 1.9k
- Information Systems 1.4k
- Computer Vision and Pattern Recognition 930
Countries citing papers authored by Philip K. Chan
This map shows the geographic impact of Philip K. Chan'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 Philip K. Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip K. Chan more than expected).
Fields of papers citing papers by Philip K. Chan
This network shows the impact of papers produced by Philip K. Chan. 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 Philip K. Chan. The network helps show where Philip K. Chan may publish in the future.
Co-authorship network of co-authors of Philip K. Chan
This figure shows the co-authorship network connecting the top 25 collaborators of Philip K. Chan. A scholar is included among the top collaborators of Philip K. Chan 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 Philip K. Chan. Philip K. Chan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. | 1 |
| 2 | Identifying Student Behaviors Early in the Term for Improving Online Course Performance. | 1 |
| 3 | 18 | |
| 4 | 33 | |
| 5 | 36 | |
| 6 | Lamotrigine adjunctive therapy to lithium and divalproex in depressed patients with rapid cycling bipolar disorder and a recent substance use disorder: a 12-week, double-blind, placebo-controlled pilot study. | 26 |
| 7 | Incrementally Learning Rules for Anomaly Detection | 2 |
| 8 | Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks | 1 |
| 9 | 36 | |
| 10 | Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms breakdown → | 492 |
| 11 | Learning States and Rules for Time Series Anomaly Detection. | 53 |
| 12 | Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security | 35 |
| 13 | Advances in Distributed and Parallel Knowledge Discovery | 129 |
| 14 | Constructing Web User Profiles: A Non-invasive Learning Approach | 6 |
| 15 | <title>Max-mean and max-median filters for detection of small targets</title> breakdown → | 607 |
| 16 | 1 | |
| 17 | AdaCost: Misclassification Cost-Sensitive Boosting | 420 |
| 18 | Toward scalable learning with non-uniform class and cost distributions: a case study in credit card fraud detection | 313 |
| 19 | Sharing learned models among remote database partitions by local meta-learning | 17 |
| 20 | A Comparative Evaluation of Combiner and Stacked Generalization | 9 |
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.