Support Vector Machines (SVMs) are known as some of the best learning models for pattern recognition, and an SVM can be used as a software reliability model to predict fault-prone modules from complexity metrics. We experimentally evaluated the prediction performance of an SVM model, comparing it with commonlyused conventional models including linear discriminant analysis, logistic regression, ...

Support Vector Machines (SVMs) are state-of-the-art algorithms for classification in machine learning. However, the SVM formulation does not directly seek to find sparse solutions. In this work, we propose an alternate formulation that explicitly imposes sparsity. We show that the proposed technique is related to the standard SVM formulation and therefore shares similar theoretical guarantees. ...

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the Vγ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e...

Support vector machines (SVM) are becoming increasingly popular for the prediction of a binary dependent variable. SVMs perform very well with respect to competing techniques. Often, the solution of an SVM is obtained by switching to the dual. In this paper, we stick to the primal support vector machine (SVM) problem, study its effective aspects, and propose varieties of convex loss functions s...

The use of SVM (Support Vector Machines) in detecting e-mail as spam or nonspam by incorporating feature selection using GA (Genetic Algorithm) is investigated. An GA approach is adopted to select features that are most favorable to SVM classifier, which is named as GA-SVM. Scaling factor is exploited to measure the relevant coefficients of feature to the classification task and is estimated by...

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of sup...

One of the most important internet challenges in coming years will be the introduction of intelligent services and a more personalized environment for user. In this paper web page prediction is presented. We use several classification techniques, namely, Support Vector Machines (SVM), Association Rule mining (ARM), and Markov model in WWW prediction. We proposed a hybrid model by combining two ...

The support vector machine was first proposed by Vapnik [1] and has since attracted a high degree of interest in the machine learning research community. Several recent studies have reported that the SVM (support vector machines) generally are capable of delivering higher performance in terms of classification accuracy than the other data classification algorithms. However, for some datasets, t...

In this paper, we deal with the stability of support vector machines (SVMs) in classification tasks. We decompose the average prediction error of support vector machines into the bias and the variance terms, and we define the aggregation effect. By estimating the aforementioned terms with bootstrap smoothing techniques, we demonstrate that support vector machines are stable classifiers. To inve...