RVM Resources

Showing items tagged with "RVM"

The Relevance Vector Machine (RVM) is a recently introduced machine learning method applicable to both classification and regression tasks. Compared to the well-established Support Vector Machine (SVM), RVM maintains excellent classification and regression performance while offering superior sparsity, resulting in enhanced generalization capabilities. This algorithm provides valuable insights for researchers in the machine learning field, with implementation advantages such as probabilistic outputs and automatic relevance determination through Bayesian inference.

MATLAB 257 views Tagged
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MATLAB source code for Relevance Vector Machine (RVM) featuring fast algorithm implementation, complete with code usage documentation. RVM employs a sparse probabilistic model with the same functional form as Support Vector Machines for prediction or classification tasks. The implementation highlights four key advantages: (1) Provides predictive distributions alongside point estimates, (2) Uses fewer relevance vectors for computational efficiency, (3) Requires fewer parameter estimations, (4) Supports arbitrary kernel functions without Mercer theorem constraints. The code includes optimized matrix operations and automatic relevance determination (ARD) for Bayesian inference.

MATLAB 275 views Tagged
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MATLAB source code implementation for Relevance Vector Machine (RVM) with fast algorithm, including comprehensive code usage documentation. RVM employs the same functional form as Support Vector Machines but establishes a sparse probabilistic model for prediction or classification of unknown functions. Key advantages include: (1) Providing both point estimates and predictive distributions for target variables; (2) Utilizing significantly fewer relevance vectors to reduce computational time; (3) Requiring fewer parameter estimations compared to traditional methods; (4) No restrictions on Mercer's theorem conditions for kernel functions, ensuring better adaptability.

MATLAB 190 views Tagged
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