Purdy Ho

1.5k citations
8 papers · 831 indexed · h-index 5

Purdy Ho

7 papers receiving 725 citations

Peers

Purdy Ho
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 620
  • Signal Processing 242
  • Computational Mathematics 8
  • Media Technology 68
  • Artificial Intelligence 235
Replace K. Etemad with:
K. Etemad United States
Patrice Y. Simard United States
Markus Flierl Sweden
Andrew Wagner United States
Serhat S. Bucak United States
Jiajun Wen China
Bilge Günsel Türkiye
Meizhu Liu United States
Johnny Mariéthoz Switzerland
Purdy Ho relative to K. Etemad United States K. Etemad's profile →
Citations per field
00.5×
K. Etemad · 1×
Citations per year

Countries citing papers authored by Purdy Ho

Since Specialization
Citations

This map shows the geographic impact of Purdy Ho'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 Purdy Ho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Purdy Ho more than expected).

Fields of papers citing papers by Purdy Ho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Purdy Ho. 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 Purdy Ho. The network helps show where Purdy Ho may publish in the future.

Co-authorship network

The 8 scholars most cited alongside Purdy Ho, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Purdy Ho Line = papers co-authored together Purdy Ho links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 20060
2 20043
3
A Generative Model Based Kernel for SVM Classification in Multimedia Applications
20044
4
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications
2003261
5 2003262
6 200331
7
Handheld Face Identification Technology in a Pervasive Computing Environment
200211
8 2002259

About Purdy Ho

Purdy Ho is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering and Infectious Diseases, having authored 8 papers that have together received 831 indexed citations. Recurring topics across this work include Speech and Audio Processing (4 papers), Speech Recognition and Synthesis (4 papers), Music and Audio Processing (3 papers), Face recognition and analysis (3 papers), Face and Expression Recognition (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Image Retrieval and Classification Techniques (1 paper) and Indoor and Outdoor Localization Technologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (620 citations), Signal Processing (242 citations), Computational Mathematics (8 citations), Media Technology (68 citations) and Artificial Intelligence (235 citations). Purdy Ho has collaborated with scholars based in United States. Frequent co-authors include Bernd Heisele, Tomaso Poggio, Pedro J. Moreno, Nuno Vasconcelos, Jane Y. Wu, Anant Agarwal, Pedro Moreno and Eugene Weinstein. Their work appears in journals such as Computer Vision and Image Understanding, The Journal of the Acoustical Society of America and Neural Information Processing Systems.

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.

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