In the customer-centered marketplace, the understanding of customer behavior is a critical success factor. The big databases in an organization usually involve multiplex data such as static, time series, symbolic sequential and textual data which are separately stored in different databases of different sections. It poses a challenge to traditional centralized customer behavior prediction. In t...

OBJECTIVE The present contribution concentrates on the application of support vector machines (SVM) for coronary heart disease and non-coronary heart disease classification. METHODS We conducted many experiments with support vector machine and different variables of low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), total cholesterol (TC), triglycerides (...

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive informatio...

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerica...

This paper describes results concerning the robustness and generalization capabilities of kernel methods in detecting intrusions using network audit trails. We use traditional support vector machines (SVM), biased support vector machine (BSVM) and leave-one-out model selection for support vector machines (looms) for model selection. We also evaluate the impact of kernel type and parameter value...

Weighted least squares support vector machine (WLSSVM) is a robust version of least squares support vector machine (LS-SVM). It adds weights on error variables to eliminate the influence of outliers. But the weights, which largely depend on the original regression errors from unweighted LS-SVM, might be unreliable for correcting the biased estimation of LS-SVM, especially for the training data ...

In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain support vector machines that learn to extract relevant features from the input patterns or from the...

Air quality prediction is highly important in view of the health impacts caused by exposure to air pollutants in urban air. This work has presented a model based on support vector machine (SVM) technique to predict daily average carbon monoxide (CO) concentrations in the atmosphere of Tehran. Two types of SVM regression models, i.e. -SVM and -SVM techniques, were used to predict average daily C...

Universum-support vector machine (U-SVM) is an elegant method for 2-class classification problem. It is systematically studied in this paper, including the existence and uniqueness of the primal problem as well as the relation between the solutions of primal problem and dual problem. We find that U-SVM uses 3-class classification approach to solve the 2-class classification problem. So we have ...