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SVM-image-classification

于 2013-04-04 发布 文件大小:1588KB
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  实现了 SVM 在医学图像分类中的应用,通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究,结合量子行为的粒子群算法的优点,提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型,应用于医学图像的分类中。 深入研究了 K-means 算法,结合了无监督聚类和监督分类方法的优点,提出了一种 K-means 和 SVM 结合的混合分类模型,并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进,并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型,并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

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