a simple and rapid method for the determination of 137ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (icp-oes) coupled with least-squares support vector machine regression (ls-svm) is reported. by evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of 137ba a...

The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance. Yet, one of the remaining drawbacks of the SVM algorithm is its high computational dem...

With the growth of different search engines, it becomes difficult for an user to search particular information effectively. If a search engine could provide domain specific information such as that confines only to a particular topicality, it is referred to as domain specific engine. Applying the ranking model trained for broad-based search to a domain specific search does not achieve good perf...

The optimal parameters of the support vector machine (SVM) are very important for accuracy modeling and generalization performance. The quantum particle swarm optimization (QPSO) algorithm takes on the characteristics of the rapid global optimization, scale chaos method provides the characteristics of the fast convergence and the SVM has the characteristics of the nonlinear fitting. These advan...

The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition di...

-The stock market is a very complex system, so it is necessary to use the support vector machine (SVM) algorithm with small sample learning characteristics. The stock market is also a chaotic system, whose financial time series data has chaotic characteristics of random, noise and strong nonlinear. However, the support vector machine for a given time series is usually not considered its chaotic...