Support vector machines are increasingly being used for problems involving both regression and classification. An inherent limitation of the original model formulation meant that the models output could not be interpreted probabilistic framework. This issue has recently been addressed, so that...

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six...

We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes...

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the...

We present a new aggregation method - called SVM algorithm - and use this technique to produce novel measures of democracy (186 countries, 1960-2014). The method takes its name from a machine learning technique for pattern recognition and has three notable features: it makes functional...