Relevance Vector Machines
Relevance Vector Machines is a sparse Bayesian learning model that, as a probabilistic counterpart to Support Vector Machines, identifies a minimal set of relevance vectors to achieve parsimonious regression and classification by leveraging Bayesian inference for automatic relevance determination.
685
Publications
41.8K
Citations
2K
Authors
732
Institutions
Publications per year
2017–2026
275
Parents
685
Sparse Bayesian Learning for Basis Selection
David Wipf, Bhaskar D. Rao · IEEE Transactions on Signal Processing · 2004 · 1.5K citations
Support vector machine active learning for image retrieval
Simon Tong, Edward Yi Chang · 2001 · 1.3K citations
Michael E. Tipping · 1999 · 875 citations
1–5 of 685
2K
15
h-index
12
h-index
9
h-index
7
h-index
6
h-index
1–5 of 2K
732

Harbin Institute of Technology
Harbin, China
18 h-index
67 publications
4.9K citations
Vellore Institute of Technology University
Vellore, India
12 h-index
21 publications
997 citations

Zhejiang University
Hangzhou, China
12 h-index
45 publications
2.9K citations

Utah State University
Logan, United States
12 h-index
41 publications
2.4K citations

Huazhong University of Science and Technology
Wuhan, China
11 h-index
22 publications
1.7K citations

North China Electric Power University
Beijing, China
10 h-index
32 publications
900 citations
University of California San Diego
San Diego, United States
10 h-index
23 publications
4.4K citations

University of Macau
Macao, Macao
10 h-index
28 publications
1.5K citations
1–8 of 732
13 publications · 506 citations
13
h-index
13 publications · 549 citations
12
h-index
9 publications · 241 citations
9
h-index
8 publications · 690 citations
8
h-index
7 publications · 217 citations
7
h-index
1–5