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It is difficult to establish accurate mathematical models to describe the range extender electric vehicles due to the non-stationary, non-linear and interconnection of the monitoring signal sources resulted from the massive moving parts and complex architecture in range-extender. And the support vector machine (SVM) and other algorithms would lead to the destruction of the natural structure and the...
In this paper, a hybrid switching particle swarm optimization (SPSO) and support vector machine (SVM) algorithm is proposed for jointly applying to the problem of bankruptcy prediction. The main purpose of this paper is to handle better explanatory power and stability of the SVM. More specifically, a recently developed Switching PSO algorithm is used to find out the optimal parameter values of radial...
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
The objective of this study concerns the classification of a scene observed by different types of images, which generates large amounts of data to be processed. We have therefore chosen to use the classification SVM (Support Vector Machines) who is known for treating high-dimensional data. Although different sources of information can provide additional information to address the ambiguities, they...
Hierarchical classification problems have been wide investigated in the past years. The available hierarchical classification methods, which use the top-down level-based scheme, often suffer from the burden of inter-level error transmission. In this paper, an instance-centric hierarchical classification framework based on decision-theoretic rough set model is proposed. The procedure of classification...
In this paper, the multi-kernel SVM (Support Vector Machine) classification, integrated with a fusion process, is proposed to segment brain tumor from multi-sequence MRI images (T2, PD, FLAIR). The objective is to quantify the evolution of a tumor during a therapeutic treatment. As the procedure develops, a manual learning process about the tumor is carried out just on the first MRI examination. Then...
Tumor segmentation, a significant application in the field of medical imaging and pattern recognition, is still a very difficult and unsolved problem up to now. In this paper, an improved SVM algorithm-multi-kernel SVM, integrated with data fusion process, is proposed to segment the tumors from the MRI image sequence. Three kinds of MRI image sequence-T2, PD, FLAIR are used as input sources in learning...
The support vector machine (SVM) is an algorithm based on structure risk minimizing principle and has high generalization ability. The model offers a kind of effective way for the information fusion problem of little sample, non-linear and high dimension. In this paper, mobile agent is applied to information fusion system. The model of OODA and the study method of information fusion system are improved...
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