Peg Howland
Impact in
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- Face and Expression Recognition
- Image Retrieval and Classification Techniques
- Computational Mathematics top 10%
Papers in
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- Face and Expression Recognition 8
- Image Retrieval and Classification Techniques 5
- Advanced Image and Video Retrieval Techniques 1
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- Text and Document Classification Technologies 3
- Neural Networks and Applications 2
- Co-authors
- Haesun Park (9 shared papers)H. Park (1 shared paper)Hyunsoo Kim (2 shared papers)Moongu Jeon (2 shared papers)Jianlin Wang (1 shared paper)Todd Munson (1 shared paper)
- Journals
- SIAM Journal on Matrix Analysis and Applications (1 paper)Pattern Recognition (1 paper)Journal of Machine Learning Research (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)University of Minnesota Digital Conservancy (University of Minnesota) (4 papers)
- Partner nations
- United States
In The Last Decade
Peg Howland
10 papers receiving 653 citations
Peers
Comparison fields: 5 of 85
- Computer Vision and Pattern Recognition 477
- Computational Mathematics 9
- Signal Processing 128
- Media Technology 89
- Analytical Chemistry 92
Countries citing papers authored by Peg Howland
This map shows the geographic impact of Peg Howland'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 Peg Howland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peg Howland more than expected).
Fields of papers citing papers by Peg Howland
This network shows the impact of papers produced by Peg Howland. 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 Peg Howland. The network helps show where Peg Howland may publish in the future.
Co-authors
The 6 scholars most cited alongside Peg Howland, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 288 | |
| 2 | Dimension Reduction in Text Classification with Support Vector Machines | 2005 | 172 |
| 3 | 2003 | 144 | |
| 4 | 2005 | 96 | |
| 5 | 2004 | 14 | |
| 6 | 2006 | 6 | |
| 7 | 2004 | 5 | |
| 8 | Extension of Discriminant Analysis based on the Generalized Singular Value Decomposition | 2002 | 4 |
| 9 | 2007 | 3 | |
| 10 | Dimension Reduction for Text Data Representation Based on Cluster Structure Preserving Projection | 2001 | 1 |
| 11 | Text Classification using Support Vector Machines with Dimension Reduction | 2003 | 0 |
About Peg Howland
Peg Howland is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Analytical Chemistry, Statistics and Probability and Signal Processing, having authored 11 papers that have together received 733 indexed citations. Recurring topics across this work include Face and Expression Recognition (8 papers), Image Retrieval and Classification Techniques (5 papers), Text and Document Classification Technologies (3 papers), Spectroscopy and Chemometric Analyses (2 papers), Neural Networks and Applications (2 papers), Advanced Statistical Methods and Models (2 papers), Blind Source Separation Techniques (1 paper) and Advanced Image and Video Retrieval Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (477 citations), Computational Mathematics (9 citations), Signal Processing (128 citations), Media Technology (89 citations) and Analytical Chemistry (92 citations). Peg Howland has collaborated with scholars based in United States. Frequent co-authors include Haesun Park, H. Park, Hyunsoo Kim, Moongu Jeon, Jianlin Wang and Todd Munson. Their work appears in journals such as SIAM Journal on Matrix Analysis and Applications, Pattern Recognition, Journal of Machine Learning Research, IEEE Transactions on Pattern Analysis and Machine Intelligence and University of Minnesota Digital Conservancy (University of Minnesota).
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.