The theory of support vector machines (SVM), has its origins in the late seventies, in the work of Vapnik [16] on the theory of statistical learning. Lately it has been receiving increasing attention, and many applications as well as important theoretical results are based on this theory. In fact, Support Vector Machines are arguably the most important discovery in the area of machine learning....

This paper presents a newmodel of support vector machines (SVMs) that handle data with tolerance and uncertainty. The constraints of the SVM are converted to fuzzy inequality. Giving more relaxation to the constraints allows us to consider an importance degree for each training samples in the constraints of the SVM. The new method is called relaxed constraints support vector machines (RSVMs). A...

Support Vector Machines (SVM) is a new machine learning approach based on Statistical Learning Theory (Vapnik-Chervonenkis or VC-theory). VCtheory has a solid mathematical background for the dependencies estimation and predictive learning from finite data sets. SVM is based on the Structural Risk Minimisation principle, aiming to minimise both the empirical risk and the complexity of the model,...

In this communication, we generalize the Support Vector Machines (SVM) for regression estimation and function approximation to multi-dimensional problems. We propose a multi-dimensional Support Vector Regressor (MSVR) that uses a cost function with a hyperspherical insensitive zone, capable of obtaining better predictions than using an SVM independently for each dimension. The resolution of the...

integrally skinned asymmetric membranes based on nanocompositepolyethersulfone were prepared by the phase separation process using the supercritical co2 as a nonsolvent for the polymer solution. in present study, the effects of temperature and nanoparticle on selectivity performance and permeability of gases has beeninvestigated. it is shown that the presence of silica nanoparticles not only di...

In this paper, a new method for edge detection based on Support Vector Machines (SVM) is presented. This method improves our previous work in edge detection with SVM by reducing the execution time and upgrading the visual quality. Our work shows that a new training technique with a reduced set of vectors maintains the edge detection quality and then reduces the number of needed support vectors....

Support vector machine (SVM) is a kernel based novel pattern classification method that is significant in many areas like data mining and machine learning. A unique strength is the use of kernel function to map the data into a higher dimensional feature space. In training SVM, kernels and its parameters have very vital role for classification accuracy. Therefore, a suitable kernel design and it...

This article investigates the performance of combining support vector machines (SVM) and various feature selection strategies. Some of them are filtertype approaches: general feature selection methods independent of SVM, and some are wrapper-type methods: modifications of SVM which can be used to select features. We apply these strategies while participating at NIPS 2003 Feature Selection Chall...

Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In this paper introduce SVM. It is techniques and methodologies developed for machine learning tasks Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. S...

This paper explores the scalability issues associated with solving the Named Entity Recognition (NER) problem using Support Vector Machines (SVM) and high-dimensional features. The performance results of a set of experiments conducted using binary and multi-class SVM with increasing training data sizes are examined. The NER domain chosen for these experiments is the biomedical publications doma...