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Sparse Bayesian Learning and the Relevance Vector Machine
Michael E Tipping
Department of Mathematical Sciences
Research output
:
Contribution to journal
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Article
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peer-review
6040
Citations (SciVal)
Overview
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Dive into the research topics of 'Sparse Bayesian Learning and the Relevance Vector Machine'. Together they form a unique fingerprint.
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Computer Science
Basis Function
100%
Support Vector Machine
100%
Bayesian Framework
100%
Bayesian Learning
100%
And-States
50%
Learning Framework
50%
Sparsity
50%
Learning Algorithm
50%
Prediction Model
50%
Classification Task
50%
Sparse Solution
50%
Functional Form
50%
Nuisance Parameter
50%
Regression Task
50%
Biochemistry, Genetics and Molecular Biology
Relevance Vector Machine
100%
Bayesian Learning
100%
Support Vector Machine
66%
Solution and Solubility
33%
Psychology
Linear Model
100%
Learning Algorithm
100%
Economics, Econometrics and Finance
Bayesian
100%
Chemical Engineering
Support Vector Machine
100%