Add to ORKGIn this paper, we consider Tipping’s relevance vector machine (RVM) [1] and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call Subspace EM (SSEM). Working with a subset of active basis functions, the sparsity of the RVM solution will ensure that the number of basis functions and thereby the computational complexity is kept low. We also introduce a mean field approach to the intractable classification model that is expected to give a very good approximation to exact Bayesian inference and contains the Laplace approximation as a special case. We test the algorithms on two large data sets with O(10 3 − 10 4) examples. The results indicate that Bayesian learning of large data sets...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...
Abstract—Sparse kernel methods are very efficient in solving regression and classification problems....
Relevance vector machine (RVM) is a machine learning algorithm based on a sparse Bayesian framework,...
In this paper, we consider Tipping‘s relevance vector machine (RVM) and formalize an incremental tra...
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 ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
Abstract—Probabilistic classification vector machine (PCVM) [5] is a sparse learning approach aiming...
Traditional non-parametric statistical learning techniques are often computationally attractive, but...
In this paper, we investigate the sparsity and recognition capabilities of two approximate Bayesian ...
In this work, the relationship between the incremental version of sparse Bayesian learning (SBL) wit...
Recently, relevance vector machines (RVM) have been fashioned from a sparse Bayesian learning (SBL) ...
Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machi...
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction. We call...
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an ...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...
Abstract—Sparse kernel methods are very efficient in solving regression and classification problems....
Relevance vector machine (RVM) is a machine learning algorithm based on a sparse Bayesian framework,...
In this paper, we consider Tipping‘s relevance vector machine (RVM) and formalize an incremental tra...
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 ...
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full ...
Abstract—Probabilistic classification vector machine (PCVM) [5] is a sparse learning approach aiming...
Traditional non-parametric statistical learning techniques are often computationally attractive, but...
In this paper, we investigate the sparsity and recognition capabilities of two approximate Bayesian ...
In this work, the relationship between the incremental version of sparse Bayesian learning (SBL) wit...
Recently, relevance vector machines (RVM) have been fashioned from a sparse Bayesian learning (SBL) ...
Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machi...
In this paper we develop a new Bayesian inference method for low rank matrix reconstruction. We call...
We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an ...
Relevance vector machines (RVM) have recently attracted much interest in the research community beca...
Abstract—Sparse kernel methods are very efficient in solving regression and classification problems....
Relevance vector machine (RVM) is a machine learning algorithm based on a sparse Bayesian framework,...