Maximum Likelihood (ML) in the linear model overfits when the number of predictors (M) exceeds the number of objects (N). One of the possible solution is the Relevance vector machine (RVM) which is a form of automatic relevance detection and has gained popularity in the pattern recognition machine learning community by the famous textbook of Bishop (2006). RVM assigns individual precisions to weights of predictors which are then estimated by maximizing the marginal likelihood (type II ML or empirical Bayes). We investigated the selection properties of RVM both analytically and by experiments in a regression setting. We show analytically that RVM selects predictors when the absolute z-ratio (|least squares estimate|/standard error) exceeds 1...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...
Maximum Likelihood (ML) in the linear model overfits when the number of predictors (M) exceeds the n...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
This paper introduces a general Bayesian framework for obtaining sparse solutions to re-gression and...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...
Maximum Likelihood (ML) in the linear model overfits when the number of predictors (M) exceeds the n...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
We focus on a selection of kernel parameters in the framework of the relevance vector machine (RVM) ...
This paper introduces a general Bayesian framework for obtaining sparse solutions to re-gression and...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
International audienceWe address the problem of Bayesian variable selection for high-dimensional lin...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...