large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. as contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. in this paper, a methodology...

We compare L1 and L2 soft margin support vector machines from the standpoint of positive definiteness, the number of support vectors, and uniqueness and degeneracy of solutions. Since the Hessian matrix of L2 SVMs is positive definite, the number of support vectors for L2 SVMs is larger than or equal to the number of L1 SVMs. For L1 SVMs, if there are plural irreducible sets of support vectors,...

⎯Due to the lack of a structure way in determining the free parameters of support vector machines (SVMs), this study uses genetic algorithms (GAs) to select parameters of SVMs. In addition, the developed SVMG (support vector machine with genetic algorithms) model is applied to reliability prediction. Two numerical examples in the literature are employed to illustrate the performances of various...

The binary support vector machines (SVMs) have been extensively investigated. However their extension to a multi-classification model is still an on-going research. In this paper we present an extension of the binary support vector machines (SVMs) for the k > 2 class problems. The SVM model as originally proposed requires the construction of several binary SVM classifiers to solve the multi-cla...

Support vector machines (SVMs) have become a popular tool for discriminative classification. Powerful theoretical and computational tools for support vector machines have enabled significant improvements in pattern classification in several areas. An exciting area of recent application of support vector machines is in speech processing. A key aspect of applying SVMs to speech is to provide a SV...

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...