a method based on electrical capacitance tomography (ect) and an improved least squares support vector machine (ls-svm) is proposed for void fraction measurement of oil-gas two-phase flow. in the modeling stage, to solve the two problems in ls-svm, pruning skills are employed to make ls-svm sparse and robust; then the real-coded genetic algorithm is introduced to solve the difficult problem of ...

For least squares support vector machine (LSSVM) the lack of sparse, while the standard sparse algorithm exist a problem that it need to mark all of training data. We propose an active learning algorithm based on LSSVM to solve sparse problem. This method first construct a minimum classification LSSVM, and then calculate the uncertainty of the sample, select the closest category to mark the sam...

Supervised learning problem with Universum data is a new research subject in machine learning. Universum data, which are not belonging to any class of the classification problem of interest, has been proved very helpful in learning. For data classification with Universum data, a novel quick classifier is proposed in this paper and named as least squares Universum twin support vector machine (LS...

this paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (lssvm). two wavelet selection criteria maximum energy to shannon entropy ratio and maximum relative wavelet energy are used and compared to select an appropriate wavelet for feature extraction. the fault diagnosis method co...

In this paper, we introduce a new technique for the separation of physical and spurious modes based on an initial clustering in frequency-damping space, followed by a self-learning classification algorithm. For the classification, Least Squares Support Vector Machines are used, a Least Squares version of the theory of Support Vector Machines which maps the classification problem to a high-dimen...