This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best classification algorithm, which uses different br...

Recently, the feasibility of using support vector machines (SVMs) for multiuser detection in code division multiple access (CDMA) systems has been investigated. Previous results show that SVMs perform well with short training sequences but suffer from two drawbacks that are highly undesirable in real-time applications: the run-time complexity and the block-based learning. To deal with these pro...

This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classific...

Support vector machines (SVMs) has been accepted as a fashionable method in machine learning community, but it cannot be easily scaled to handle large scale problems for its time and space complexity that is around quadratic in the number of training samples. In this paper, confident majority voting (CMV) is proposed to scale SVMs (CMV-SVMs). CMV-SVMs decomposes a large-scale task into many sma...

The support vector machines were originally created to classify binary problems. Their extension for multiclass problems was the subject of several researches. Usually, a multiclass classifier is obtained by combining several binary classifiers. During the last years, the attention is focused on four main models of Multi-class Support Vector Machines (M-SVM), which consider all classes simultan...

We identify and explore an Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, a...

Support vector machines (SVMs) are a quite recent supervised learning approach towards function estimation. They combine several results from statistical learning theory, optimisation theory, and machine learning, and employ kernels as one of their most important ingredients. The present work covers the theory of SVMs with emphasis on SVMs for regression estimation, and the problem of chaotic t...

We present a new technique that employs support vector machines (SVMs) and Gaussian mixture densities (GMDs) to create a generative/discriminative object classification technique using local image features. In the past, several approaches to fuse the advantages of generative and discriminative approaches were presented, often leading to improved robustness and recognition accuracy. Support vect...

This paper describes results concerning the robustness and generalization capabilities of kernel methods in detecting intrusions using network audit trails. We use traditional support vector machines (SVM), biased support vector machine (BSVM) and leave-one-out model selection for support vector machines (looms) for model selection. We also evaluate the impact of kernel type and parameter value...

This paper concerns using learning machines for intrusion detection. Two classes of learning machines are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that SVMs are superior to ANNs for intrusion detection in three critical respects: SVMs train, and run, an order of magnitude faster; SVMs scale much better; and SVMs give higher classification accuracy. ...