This study presents a novel technique based on Support Vector Machine (SVM) for the classification of transient phenomena in power transformer. The SVM is a powerful method for statistical classification of data. The input data to this SVM for training comprises fault current and magnetizing inrush current. SVM classifier produces significant accuracy for classification of transient phenomena i...

INTRODUCTION Multivariate pattern classification and prediction offers an alternative to standard univariate analysis techniques, and has recently been applied in MR imaging using support vector machines (SVM) [1], and used to attain real-time feedback [2]. The standard approach has been to use reconstructed image magnitude data. However, additional information may be contained in the image pha...

According to the difference of the modeling theories, the stock yield prediction models can be divided into two sorts. One is the traditional fluctuation rate prediction model based on statistical theory, and the other is the innovational prediction model based on theories such as NN, grey theory, support vector machines (SVM) and so on. In this article, we introduced these two models and their...

The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be optimal for the original multicategor...

This paper presents a new view of the facial expression recognition problem, by addressing the question of whether or not is possible to classify previously labeled photogenic and non-photogenic face images, based on their appearance. In the proposed approach, a Support Vector Machine (SVM) is trained with Gabor representations of the face images to learn the relationships between facial expres...

Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. A comparison of a conventional method and a Support Vector Machine algorithm is presented here for the case of identifying top quark signal events in the dilepton decay channel amidst a large number of background...

Taking Harare metropolitan province in Zimbabwe as an example, we classified Landsat imagery (1984, 2002, 2008 and 2013) by using support vector machines (SVMs) and analyzed built-up and non-built-up changes. The overall classification accuracy for the four dates ranged from 89% to 95%, while the overall kappa varied from 86% to 93%. The results demonstrate that SVMs provide a cost-effective te...

With the rapid development of the Internet, P2P has become the main network application in the Internet, which consumes most of the network resources. Accurately identifying and making control of the P2P traffic is of great significance. As a mature classification theory, support vector machine (SVM) algorithm is suitable for P2P traffic identification. This paper proposes a SVM based P2P flow ...

The goal of this work is to introduce one of the most successful among recently developed statistical techniques – the support vector machine (SVM) – to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial ratios. A method is proposed of adapting SVMs to default probability estimation. A survey of practicall...

Imbalanced data sets have significantly unequal distributions between classes. This between-class imbalance causes conventional classification methods to favor majority classes, resulting in very low or even no detection of minority classes. A Min-Max modular support vector machine (M-SVM) approaches this problem by decomposing the training input sets of the majority classes into subsets of sim...