Fast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth the first time in this study. The FMRVR a analysis technique which can simultaneously predict multiple outputs multi-dimensional input. data used study collected from 34 stations located Urmia basin over 40-year period. performance model examined contrast ...

In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression. Input selection using rank-one updates for LS-SVMs performed better than automatic relevance determination. Evaluation on an independent test set showed good performance of the classifiers to distinguish between all...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

Statistical modelling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. We develop nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik’s 2-insensitive loss function, based on reprodu...

In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based on the evidence framework introduced by MacKay. The principal innovation lies in the re-parameterisation of the model such that the usual spherical Gaussian prior over the parameters in the kernel induced feature space also corresponds to a spherical Gaussian prior over t...

A regression based method is proposed to recover human body pose from 3D voxel data. In order to do this we need to convert the voxel data into a feature vector. This is done using a Bayesian approach based on Mixture of Probabilistic PCA that transforms a collection of 3D shape context descriptors, extracted from the voxels, to a compact feature vector. For the regression, the newly-proposed M...

The relevance vector machine(RVM) is a state-of-the-art constructing sparse regression kernel model [1,2,3,4]. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). In RVM and SVM, relevance vectors (RVs) and support vectors (SVs) are both selected from the input vector set. This may limit model flexibility. In ...