Support Vector Machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM nds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a sub...

This paper aims to implement a Support Vector Machines (SVMs) based text classification system for Arabic language articles. This classifier uses CHI square method as a feature selection method in the pre-processing step of the Text Classification system design procedure. Comparing to other classification methods, our system shows a high classification effectiveness for Arabic data set in term ...

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 why SVMs arc appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of...

Computational Intelligence (CI) methods are increasingly being used for problem solving. This paper concerns using CI-type learning machines for intrusion detection, which is a problem of general interest to transportation infrastructure protection since a necessary task thereof is to protect the computers responsible for the infrastructure’s operational control, and an effective Intrusion Dete...

This paper presents the use of Support Vector Machines (SVM) to detect relevant information to be included in a queryfocused summary. Several SVMs are trained using information from pyramids of summary content units. Their performance is compared with the best performing systems in DUC-2005, using both ROUGE and autoPan, an automatic scoring method for pyramid evaluation.

Participating in the BioCreative IV CTD Curation shared task, we developed RESTful, BioCcompliant web services which recognise CTD chemicals, genes, diseases and actions terms in PubMed abstracts. The tools are based on machine learning approaches, specifically, conditional random fields (CRFs) for recognising names of chemicals, genes and diseases, and support vector machines (SVMs) for recogn...

Previous work has shown that connectionist learning systems can simulate important aspects of the categorization of speech sounds by human and animal listeners. Training is on representations of synthetic, exemplar voiced and unvoiced stop consonants passed through a computational model of the auditory periphery. In this work, we use the modern inductive inference technique of support vector ma...

The VC dimension measures the complexity of a learning machine, and a low VC dimension leads to good generalization. While SVMs produce state-of-the-art learning performance, it is well known that the VC dimension of a SVM can be unbounded; despite good results in practice, there is no guarantee of good generalization. In this paper, we show how to learn a hyperplane classifier by minimizing an...

Binary hashing is an established approach for fast, approximate image search. The idea is to learn a hash function that maps a query image to a binary vector so that Hamming distances approximate image similarities. An important subproblem in binary hashing is to solve a set of independent classification problems, usually using support vector machines (SVMs). In this paper, we show that the has...

A reliable land cover (LC) map is essential for planners, as missing proper maps may deviate a project. This study focusing on classification and prediction using three well known classifiers remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), Support Vector Machines (SVMs) algorithms are used the representatives parametric, non-parametric subpixel capable met...