Variational relevance vector machine

Details Variational relevance vector machine Variational relevance vector machine

2005-04-12

2005-04-12

Phan, Thai (Department: 2128)

Data processing: structural design, modeling, simulation, and em

Modeling by mathematical expression

C706S012000, C706S016000, C706S025000

Reexamination Certificate

active

06879944

ABSTRACT:
A variational Relevance Vector Machine (RVM) is disclosed. The RVM is a probabilistic basis model. Sparsity is achieved through a Bayesian treatment, where a prior is introduced over the weights governed by a set of what are referred to as hyperparameters—one such hyperparameter associated with each weight. An approximation to the joint posterior distribution over weights and hyperparameters is iteratively estimated from the data. The posterior distribution of many of the weights is sharply peaked around zero, in practice. The variational RVM utilizes a variational approach to solve the model, in particular using product approximations to obtain the posterior distribution.

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