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...

This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines (SVM) with elitist genetic algorithm (GA). The new algorithm uses EGA, which is based on elite survival strategy, to optimize the parameters of SVM. Iris dataset and one hundred pieces of news reports in Chinese news are chosen to compare EGA-SVM, GA-SVM and traditional SVM. Th...

The purpose of this article is to introduce a new scheme for robust multivariate ranking by making use of a not so familiar notion called monotonicity. Under this scheme, as in the case of classical outward ranking, we get an increasing sequence of regions diverging away from a central region (may be a single point) as nucleus. The nuclear region may be deened as the median region.

In this paper we present a new algorithm to speed up the training time of Support Vector Machines (SVM). SVM has some important properties like solid mathematical background and a better generalization capability than other machines like for example neural networks. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent...

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized ver...

With the progress in machine translation, it becomes more subtle to develop the evaluation metric capturing the systems’ differences in comparison to the human translations. In contrast to the current efforts in leveraging more linguistic information to depict translation quality, this paper takes the thread of combining language independent features for a robust solution to MT evaluation metri...

According to the defects of KNN(K-Nearest Neighbor) algorithm and SVM(Support Vector Machine) algorithm in tracking a moving target such the large consumption and the low accuracy of target tracking error, a tracking model of moving target is proposed based on the combination of KNN algorithm and SVM algorithm with minimum distance optimization. First categories divided according to the princip...

Over the past two decades, support vector machine (SVM) has become a popular supervised learning model, and plenty of distinct algorithms are designed separately based on different KKT conditions SVM model for classification/regression with losses, including convex loss or nonconvex loss. In this paper, we propose an algorithm that can train models in \emph{unified} scheme. First, introduce def...

Robust cancer molecular pattern identification from microarray data not only plays an essential role in modern clinic oncology, but also presents a challenge for statistical learning. Although principal component analysis (PCA) is a widely used feature selection algorithm in microarray analysis, its holistic mechanism prevents it from capturing the latent local data structure in the following c...