In this work we first propose a heteroscedastic generalization to RVM, a fast Bayesian framework for regression, based on some recent similar works. We use variational approximation and expectation propagation to tackle the problem. The work is still under progress and we are examining the results and comparing with the previous works.

The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of probabilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this pap...

Handing unbalanced data and noise are two important issues in the field of machine learning. This paper proposed a complete framework of fuzzy relevance vector machine by weighting the punishment terms of error in Bayesian inference process of relevance vector machine (RVM). Above problems can be learned within this framework with different kinds of fuzzy membership functions. Experiments on bo...

The Relevance Vector Machine (RVM) algorithm has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensitive applications by utilizing massively parallel accelerators such as GPUs. In this paper, the computati...

The objective and automated monitoring of depression using behavioral signals is confounded by the wide clinical profile of this commonly occurring mood disorder. This paper introduces Relevance Vector Machines, a novel method for predicting clinical depression scores from paralinguistic cues. It highlights many of the advantages RVM can offer depression prediction; sparsity, implicit noise cha...

The relevance vector machine (RVM) is a Bayesian framework for learning sparse regression models and classifiers. Despite of its popularity and practical success, no thorough analysis of its functionality exists. In this paper we consider the RVM in the case of regression models and present two kinds of analysis results: we derive a full characterization of the behavior of the RVM analytically ...