support vector machine (svm) was used to analyze the occurrence of roach in flemish stream basins (belgium). several habitat and physico?chemical variables were used as inputs for the model development. the biotic variable merely consisted of abundance data which was used for predicting presence/absence of roach. genetic algorithm (ga) was combined with svm in order to select the most important...

the lithologies of regions, which located near the collision zone, are very different from other geology setting. mapping in these areas needs extensive and exact studies and tools because of the variety of rocks, intensive tectonic uplift and complicated units. hyperspectral sensor is one of the most advanced tools with hundreds of bands that each measures a very narrow range of wavelengths an...

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...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

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...

In this paper, we address the problem of image annotation with incomplete labelling, where the multiple objects in each training image are not fully labeled. The conventional one-versus-all SVM (OVASVM) that performs fairly well on full labelling decays drastically under the incomplete setting. Recently, structured learning method termed OVA-SSVM is proposed to boost the performance of OVA-SVM ...