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Support vector machine (SVM) is a popular supervised learning algorithm achieved much success in classification problems in both single output and multi-output cases. Despite this fact, SVM has some limitations, for example, in terms of the accuracy when data are not linearly separable. One of the well-known tricks for tackling this obstacle is using kernel functions in the base model of SVM which map all data into a higher or infinite dimension space called feature space. We propose polar coordinate system as an efficient technique for mapping data and improving the accuracy of SVM in some datasets. Hence, the current research is assigned to extending SVM model to polar one for single and multi-output cases. Also, the proposed SVM may be combined with kernel functions to outperform traditional non-linear SVM. The performance of the proposed model is compared with traditional one to affirm the superiority of polar SVM. [ABSTRACT FROM AUTHOR]

Adaptive control charts have been developed for improving the capability of control charts in detecting small shifts. In this article, we propose a new exponential weighted moving average control chart with variable sample size, in which the sample size is determined as an integer linear function by EWMA statistic value. The performance of the proposed VSS EWMA control chart is compared with FSS EWMA as well as traditional VSS EWMA control charts. The results show the better performance of the proposed VSS strategy respect to the traditional one and fixed sample size. [ABSTRACT FROM PUBLISHER]