Proximal SVM (PSVM), which is a variation of standard SVM, leads to an extremely faster and simpler algorithm for generating a linear or nonlinear classifier than classical SVM. An efficient incremental method for linear PSVM classifier has been introduced, but it can’t apply to nonlinear PSVM and incremental technique is the base of online learning and large data set training. In this paper we...

Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time. To improve scalability, we have developed a parallel SVM algorithm (PSVM), which reduces memory use through performing a row-based, approximate matrix factorization, and which loads only essential data to each machine to perform parallel computation. Let n denote the num...

Nonlinear Support Vector Machines employ sophisticated kernel functions to classify data sets with complex decision surfaces. Determining the right parameters of such functions is not only computationally expensive, the resulting models are also susceptible to overfitting due to their large VC dimensions. Instead of fitting a nonlinear model, this paper presents a framework called Localized Sup...

Machine learning applied to large-scale remote sensing images shows inadequacies in computational capability and storage space. To solve this problem, we propose a cloud computing-based scheme for learning remote sensing images in a parallel manner: (1) a hull vector-based hybrid parallel support vector machine model (HHB-PSVM) is proposed. It can substantially improve the efficiency of trainin...

Résumé. Les algorithmes de boosting de Newton Support Vector Machine (NSVM), Proximal Support Vector Machine (PSVM) et Least-Squares Support Vector Machine (LS-SVM) que nous présentons visent à la classification de très grands ensembles de données sur des machines standard. Nous présentons une extension des algorithmes de NSVM, PSVM et LS-SVM, pour construire des algorithmes de boosting. A cett...

This paper presents a methodology to calculate probabilities of failure using Probabilistic Support Vector Machines (PSVMs). Support Vector Machines (SVMs) have recently gained attention for reliability assessment because of several inherent advantages. Specifically, SVMs allow one to construct explicitly the boundary of a failure domain. In addition, they provide a technical solution for probl...

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient...

Making speaker verification (SV) systems robust to spoofed/mimicked speech attacks is very important to make its use effective in security applications. In this work, we show that using a proximal support vector machine backend classifier with i-vectors as inputs (i-PSVM) can help improve the performance of SV systems for mimicked speech as non-target trials. We compared our results with the st...

Parameter selection is one of the important steps involved in any model fitting. In this paper we have used Uniform Design Tables to choose the parameters for PSVM and SVM to classify the data. UD is one of the efficient space filling designs, which spreads the combination of parameters in the space uniformly scattered and generalizes the performance of the model efficiently. This paper compare...