This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and for these criteria we have compared different search space algorithms: recursive feature elimination and scaling factors optimization based on gradient descent. All these algorithms have been compared on some toy prob...

Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction; hence its test speed is slower than other learning algorithms such as support vector machines (SVMs), relevance vector machine (RVM), adaptive sparseness (AS), etc. To overcome this limitation, we present a backward elimination algorithm, called GPs-BE that recu...

In this chapter, we develop and evaluate a feature selection algorithm for Bayesian support vector machines. The relevance level of features are represented by ARD (automatic relevance determination) parameters, which are optimized by maximizing the model evidence in the Bayesian framework. The features are ranked in descending order using the optimal ARD values, and then forward selection is c...

abstract in the recent decades, the iranian economy has been highly depended on oil revenues. considering the fact that a great part of non-oil exports are agricultures product, studying factors influencing growth of agricultural sector plays an important role in the iran's economy. supply domestic shocks and domestic demand pressure along with deviation of exchange rates from its equilibrium, ...

background: we aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (lr), decision tree (dt), artificial neural network (ann), and support vector machine (svm). methods: we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province, the west of iran, in 2010. to evaluate the high-risk grou...

Abstract. A new supervised adaptive metric approach is introduced for mapping an input vector space to a plottable low-dimensional subspace in which the pairwise distances are in maximum correlation with distances of the associated target space. The formalism of multivariate subspace regression (MSR) is based on cost function optimization, and it allows assessing the relevance of input vector a...

Kernel-based machine learning algorithms are based on mapping data from the original input feature space to a kernel feature space of higher dimensionality to solve a linear problem in that space. Over the last decade, kernel based classification and regression approaches such as support vector machines have widely been used in remote sensing as well as in various civil engineering applications...

klinkenberg permeability is an important parameter in tight gas reservoirs. there are conventional methods for determining it, but these methods depend on core permeability. cores are few in number, but well logs are usually accessible for all wells and provide continuous information. in this regard, regression methods have been used to achieve reliable relations between log readings and klinke...