Abstract Support Vector Machines (SVM) are an efficient alternative for supervised
classification. In the soft margin SVM model, two different objectives are optimized and the …

Support vector machine (SVM) is a well-regarded machine learning algorithm widely
applied to classification tasks and regression problems. SVM was founded based on the …

We study the impact of network information for social security fraud detection. In a social
security system, companies have to pay taxes to the government. This study aims to identify …

Abstract The Supervised Classification problem, one of the oldest and most recurrent
problems in applied data analysis, has always been analyzed from many different …

Support vector machine (SVM) is a widely used and effective classifier. Its efficiency and
accuracy mainly depend on the exceptional feature subset and optimal parameters. In this …

In spite of the growing opportunities and demands for using social media to assist
government decision-making, few studies have investigated social media sentiments toward …

The paper presents an overview of global issues in optimization methods for training
feedforward neural networks (FNN) in a regression setting. We first recall the learning …

The performance of support vector machine (SVM) heavily depends on its parameters. The
parameter optimization for SVM is still an ongoing research issue. The current parameter …

The paper investigates statistical models for forecasting the resale prices of used cars. An
empirical study is performed to explore the contributions of different degrees of freedom in …

Abstract The Support Vector Machine (SVM) is a supervised learning algorithm to analyze
data and recognize patterns. The standard SVM suffers from some limitations in nonlinear …