In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Embedded systems technology is undergoing a phase of transformation owing to the novel
advancements in computer architecture and the breakthroughs in machine learning …

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function
(RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm …

The extraction of useful insights from text with various types of statistical algorithms is
referred to as text mining, text analytics, or machine learning from text. The choice of …

Abstract Machine learning techniques are a set of mathematical models to solve high non-
linearity problems of different topics: prediction, classification, data association, data …

The intrusion detection system is a method for detection against attacks, making it one of the
essential defense layers. Researchers are trying to find new algorithms to inspect all …

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

The tutorial starts with an overview of the concepts of VC dimension and structural risk
minimization. We then describe linear Support Vector Machines (SVMs) for separable and …

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and
methods for mining patterns, knowledge, and models from various kinds of data for diverse …

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers
from examples. It analyzes the particular properties of learning with text data and identifies …