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中文摘要：
支持向量机（SVM）是一种准确度高的分类器,具有很好的容错和归纳能力;粗糙集理论方法在处理大数据量、消除冗余信息等方面具有优势。将两者相结合提出一种改进的SVM分类算法ISVM,并将其应用于乳腺X光图像分类。实验结果表明,ISVM的分类精确度可达到96.56%,比SVM的分类精确度（92.94%）要高3.42%,同时错误分辨率也平均接近100%。
英文摘要：
Support vector machine（SVM） has high classify accuracy and good capabilities of fault-tolerance and generalization. The rough sets theory approach has the advantages on dealing with great data and eliminating redundant information. This paper joined the SVM classifier with rough sets theory which called the improved SVM （ISVM） to classify digital mammography. The experimental results show that the improved SVM classifier can get 96.56% accuracy which is higher about 3.42% than 92.94% using SVM, and the error recognition rates are closed to 100% averagely.
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