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...

Water quality directly determines our living environment. In order to establish a more scientific and reasonable water evaluation model, it needs lot of data support, but will lead large increase in the calculation time model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) solve this problem. paper, Pareto optimal solution concept is used sparsely process train...

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...

We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a hierarchical SVM classifier. Experimental results in face classification show considerable robustness ...

Binary classification is the problem of predicting the class a given sample belongs to. To achieve a good prediction performance, it is important to find a suitable model for a given dataset. However, it is often time consuming and impractical for practitioners to try various classification models because each model employs a different formulation and algorithm. The difficulty can be mitigated ...

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...

Bots, which are new malignant programs are hard to detect by signature based pattern matching techniques. In this research, we focused on a unique function of the bots the remote control channel (C&C session). We clarified that the C&C session has unique characteristics that come from the behavior of bot programs. Accordingly, we propose an alternative technique to identify computers compromise...