Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-ofthe-art learning model...

This paper proposes a new type of a support vector machine which uses a kernel constituted from fuzzy basis functions. The proposed network combines the characteristics both of a support vector machine and a fuzzy system: high generalization performance, even when the dimension of the input space is very high, structured and numerical representation of knowledge and ability to extract linguisti...

Many of the classification algorithms used in practice today are based on extensions of the binary SVM classifier, which has been very successful, especially in the field of bioinformatics. SVMs run on information derived from the convex hulls of the initial training data, ignoring the data points inside the convex hull. This paper presents a new way of deriving the separating hyperplane used i...

Considerable efforts have been devoted to the implementation of efficient optimization method for solving the Support Vector Machine dual problem. This document proposes an historical perspective and and in depth review of the algorithmic and computational issues associated with this problem.

Support Vector Machine (SVM) is one of the most popular classifiers in pattern recognition, which aims to find a hyperplane that can separate two classes of samples with the maximal margin. As a result, traditional SVM usually more focuses on the scatter between classes, but neglects the different data distributions within classes which are also vital for an optimal classifier in different real...

In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. Modern vehicles have 40 to 80 electronic control units interconnected via the so-called in-vehicle network. The communication on this in-vehicle network is recorded during test drives for the use of fault analysis, which results...

In recent years,machine learning method has been applied to the extensive research on traffic classification. In these methods, SVM (Support vector machine) is a supervised learning which can improve generalization ability of learning machine effectively. However, the penalty parameter C and kernel function parameter  are generally given by test experience during training of SVM. How to determ...

The growth in the number of mobile subscriptions has led to a substantial increase in the mobile network bandwidth demand. The mobile network operators need to provide enough resources to meet the huge network demand and provide a satisfactory level of Quality-of-Service (QoS) to their users. However, in order to reduce the cost, the network operators need an efficient network plan that helps t...

Support vector machine (SVM) is one of the most important machine learning algorithms that has been implemented mostly in pattern recognition problem, for e.g. classifying the network traffic and also in image processing for recognition . Lots of research is going on in this technique for the improvement of Qos (quality of service) and in security perspective. The latest works in this field hav...