this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. the support vector machine (svm), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. the work present...

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...

Training support vector machines (SVMs) with nonlinear kernel functions on large-scale data are usually very timeconsuming. In contrast, there exist faster solvers to train the linear SVM. We propose a technique which sufficiently approximates the infinite-dimensional implicit feature mapping of the Gaussian kernel function by a low-dimensional feature mapping. By explicitly mapping data to the...

Support vector machines (SVM) have been promising methods for classification analysis due to their solid mathematical foundations. Clustering-based SVMs are used to solve large samples classification problems and reduce the computational cost. In this paper, we present a density clustering based SVM(DCB-SVM) method to predict polyadenylation signal (PAS) in human DNA and mRNA sequences. We decr...

SVM is extensively used in pattern recognition because of its capability to classify future unseen data and its’ good generalization performance. Several algorithms and models have been proposed for pattern recognition that uses SVM for classification. These models proved the efficiency of SVM in pattern recognition. Researchers have compared their results for SVM with other traditional empiric...

Support Vector Machine (SVM) is a classification technique based on Structural Risk Minimization (SRM), which can run on MATLAB. For classification of nonseparable samples, conventional SVM needs to select a tradeoff between maximization the margin and misclassification rate. In order to guarantee generalized performance and low misclassification rate of SVM, this paper puts forward an improved...

In this paper, we propose a novel method based on support vector machine (SVM) for microarray classification and gene (feature) selection. The proposed method, called similaritybased SVM (SSVM), incorporates the prior knowledge of gene similarity into the standard SVM by combining the standard l2 norm and the similarity penalty of all the genes. The preliminary experiments show that our method ...

Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SV...