Lappoon R. Tang
- Artificial Intelligence top 5%
- Computational Theory and Mathematics
- Information Systems
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Vladimir LifschitzHudson TurnerRaymond J. MooneyJude ShavlikVı́tor Santos CostaPrem MelvilleDavid PageHansheng Lei
- Topics
- Data Mining Algorithms and Applications (3 papers)Multi-Agent Systems and Negotiation (1 paper)Logic, Reasoning, and Knowledge (1 paper)
- Journals
- Classical and Quantum GravityAnnals of Mathematics and Artificial IntelligenceScholarWorks @ UTRGV (The University of Texas Rio Grande Valley)
- Partner nations
- United States
In The Last Decade
Lappoon R. Tang
6 papers receiving 207 citations
Peers
Comparison fields: 5 of 25
- Artificial Intelligence 212
- Computational Theory and Mathematics 33
- Information Systems 30
- Computer Networks and Communications 16
- Signal Processing 15
Countries citing papers authored by Lappoon R. Tang
This map shows the geographic impact of Lappoon R. Tang'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 Lappoon R. Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lappoon R. Tang more than expected).
Fields of papers citing papers by Lappoon R. Tang
This network shows the impact of papers produced by Lappoon R. Tang. 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 Lappoon R. Tang. The network helps show where Lappoon R. Tang may publish in the future.
Co-authorship network of co-authors of Lappoon R. Tang
This figure shows the co-authorship network connecting the top 25 collaborators of Lappoon R. Tang. A scholar is included among the top collaborators of Lappoon R. Tang 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 Lappoon R. Tang. Lappoon R. Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | S-means: Similarity Driven Clustering and Its application in Gravitational-Wave Astronomy Data Mining | 6 |
| 2 | 5 | |
| 3 | Relational Data Mining with Inductive Logic Programming for Link Discovery | 34 |
| 4 | 50 | |
| 5 | Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases | 0 |
| 6 | 131 | |
| 7 | An Experimental Comparison of Genetic Programming and Inductive LogicProgramming on Learning Recursive List Functions | 4 |
About Lappoon R. Tang
Lappoon R. Tang is a scholar working on Signal Processing, Information Systems and Artificial Intelligence, having authored 7 papers that have together received 230 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (3 papers), Multi-Agent Systems and Negotiation (1 paper) and Logic, Reasoning, and Knowledge (1 paper). The work is most often cited by research in Artificial Intelligence (212 citations), Computational Theory and Mathematics (33 citations) and Signal Processing (15 citations). Lappoon R. Tang has collaborated with scholars based in United States. Frequent co-authors include Vladimir Lifschitz, Hudson Turner, Raymond J. Mooney, Jude Shavlik, Vı́tor Santos Costa, Prem Melville, David Page, Hansheng Lei, Soumya D. Mohanty and Mary Elaine Califf. Their work appears in journals such as Classical and Quantum Gravity, Annals of Mathematics and Artificial Intelligence and ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley).
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.