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 ...

This paper aims to decrease the time complexity of multi-output relevance vector regression from O ( VM ) to O ( V 3 +M ) , where V is the number of output dimensions, M is the number of basis functions, and V < M . The experimental results demonstrate that the proposed method is more competitive than the existing method, with regard to computation time. MATLAB codes are available at http://www...

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...

key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there ...

Relevance vector regression (RVR) is a useful tool for degradation modeling and remaining life (RUL) prediction. However, most RVR models are 1-D processes can only handle univariate observations. This article proposes path-based RUL prediction framework using dynamic multivariate relevance model. Specifically, multistep model established describing the dynamics extending classical into one wit...

We present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal brain images, to help evaluate pathological stage and predict future progression of diseases, e.g., Alzheimer’s diseases (AD). Different from most existing methods, we predict clinical scores from multimodal (imaging and biological) bi...