the prediction of lithology is necessary in all areas of petroleum engineering. this means that todesign a project in any branch of petroleum engineering, the lithology must be well known. supportvector machines (svm’s) use an analytical approach to classification based on statistical learningtheory, the principles of structural risk minimization, and empirical risk minimization. in thisresearc...

this paper aims to propose an effective paroxysmal atrial fibrillation (paf) predictor which is based on the analysis of the heart rate variability (hrv) signal. predicting the onset of paf, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize the risks for the patients. this method consists of four st...

To investigate the detection of rice exterior quality, a machine vision system was developed. The main characteristics of rice appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least Squares Support Vector Machines, Naive Bayes Classifier and Back Propagation Artificial Neural Network were applied to achieve classification of head...

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines SVMs have been applied to the problem of financial early-warning prediction Rose, 1999 . The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the ...

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest...

Support vector machines (SVMs) are a very popular method for binary classification. Traditional training algorithms for SVMs, such as chunking and SMO, scale superlinearly with the number of examples, which quickly becomes infeasible for large training sets. Since it has been commonly observed that dataset sizes have been growing steadily larger over the past few years, this necessitates the de...

We propose a novel learning technique for classification as result of the hybridization between support vector machines and evolutionary algorithms. Evolutionary support vector machines consider the classification task as in support vector machines but use evolutionary algorithms to solve the optimization problem of determining the decision function. They can acquire the coefficients of the sep...

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...