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Personal credit assessment is carried out by setting up a mathematical model to count, calculate and analyze the personal credit data. At present personal credit assessment has already became a kind of worldwide industry. In this paper we combine kernel principal component analysis and support vector machine to propose a new mathematical model based on KPCA and SVM. We extract personal credit data...
Traditional classification algorithms used in remote sensing images have many problems, such as the low operation speed, low accuracy and difficult convergence. Support Vector Machine (SVM) is a new machine learning method of statistical learning theory based on small samples of machine learning rules. This paper deals with the remote sensing image classification by the support vector machine, using...
This paper presents a new algorithm of Web page classification, CUCS(Combined UC and SVM), for large training set. CUCS combines the advantages of SVM (Support Vector Machine) and UC (Unsupervised Clustering), achieving high precision and fast speed. In the training stage, CUCS gets clustering centers, which include positive example centers and negative ones, by means of UC. Then CUCS prunes training...
A novel method is presented in this paper to study the use of SVM classifiers for multiple feature classification. While commonly multiple binary SVM classifiers are trained on features individually and the outputs of the classifiers are linearly combined for multiple feature classification, our method trains and combines these classifiers simultaneously with lower complexity. To obtain the optimal/suboptimal...
This paper proposes an effective method for constructing and pruning support vector machine ensembles for improved classification performance. Firstly we propose a novel method for constructing SVM ensembles. Traditionally an SVM ensemble is constructed by the data sampling method; In our method, however,each individual SVM classifier is trained by using the same original training set, but with different...
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