Through concept generalization and deleting irrelevant dimension, various association rules less than 7d can be evolved from the 7d image association rules model 7d_ar.

通过概念提升及删除无关维，由七维图像关联规则模型7d_ar可以演化出维数小于七维的各种关联则。

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The experimental results show that this method leads to statistical models with better generalization ability, which is useful for cardiac image segmentation and motion analysis.

实验结果表明，该方法可有效提高统计模型的泛化能力，并实现对心室结构的分割和运动跟踪。

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Kernel-based Support Vector Machine (SVM) is widely used in many fields (e. g. image classification) for its good generalization, in which the key factor is to design effective kernel functions.

基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用，运用支持向量机的关键是设计有效的核函数。

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