Microcalcification Detection Based on K-means Cluster and Multiple Kernel Support Vector Machine
基于K均值聚类和多核SVM的微钙化簇检测

Considering the unbalanced distribution of the training samples and the multiformity of the features.A multiple kernel SVM based on K-means cluster algorithm was proposed.Firstly,training samples was clustered into K classes,different penalty factors were used for each class in order to balance the contributions of each class.Secondly,the multiple kernel support vector machine was proposed for diversity of the features.The stabilized training sample was obtained via active feedback learning.The result show ...