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Breast cancer is the most common malignant tumor for women. In the past twenty years, the incidence of breast cancer continues to rise. Then, the diagnosis and treatment of the breast cancer have become an extremely urgent work to do. In this study, we intend to build a diagnostic model of breast cancer by using data mining techniques. A feature selection method, INTERACT is applied to select relevant...
In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods...
Feature selection plays an important role in the area of machine learning. Class Label is often used as the supervised information for supervised feature selection algorithm while constraints are rarely used. So, an effective feature selection algorithm with pairwise constraints called Constraints Score was proposed. But its performance still is limited by neglecting the correlation between features...
We present a fully automatic approach to learning the personal facial attractiveness preferences of individual users directly from example images. The target application is computer assisted search of partners in online dating services. The proposed approach is based on the use of epsiv-SVMs to learn a regression function that maps low level image features onto attractiveness ratings. We present empirical...
During IC photomask vision inspection, considering problem that fine image defectpsilas fineness, complex shape, extraction feature difficultly, and effect by noise easily, presented defect identification classification algorithm based on PCA (principal components analysis) and SVM (support vector machine). It resolved the problem that fine and complex defect was difficult to classify, by merits of...
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