| 陈婉茹.基于Seeded-Kmeans和SVM的分类算法[J].西昌学院学报（自然科学版）,2023,37(3):41-45. |
| 基于Seeded-Kmeans和SVM的分类算法 |
| A Classification Algorithm Based on Seeded-Kmeans and SVM |
| 投稿时间：2023-04-19 修订日期：2023-05-30 |
| DOI：10.16104/j.issn.1673-1891.2023.03.007 |
| 中文关键词: k-means算法 seeded-kmeans 支持向量机（SVM） 半监督支持向量机（S3VM） |
| 英文关键词: kmeans algorithm seeded-kmeans support vector machines (SVM) semi-supervised support vector machines (S3VM) |
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| 摘要点击次数: 190 |
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| 中文摘要: |
| 支持向量机（Support vector machines）在人像识别、文本分类等模式识别问题中有广泛的应用，可以有效地解决一些实际生活中的分类问题。针对半监督两分类问题，提出了基于Seeded-Kmeans和SVM的分类算法（SK-SVM）。用Seeded-Kmeans算法对无标签点进行处理，使其获得初始标签，再选取有效的标签点加入已有带标签点中，构成新的带标签训练集，最后结合SVM进行分类。选取UCI中的8个数据集进行数值实验，基于Seeded-Kmeans和SVM的分类算法的有效性得到了验证。 |
| 英文摘要: |
| Support vector machine is widely used in pattern recognition problems such as the portrait recognition and the text classification recognition. It can effectively solve some classification problems in real life. In this paper, a classification algorithm based on Seeded-Kmeans and SVM （SK-SVM） is proposed for the semi-supervised two classification problem. The Seeded-Kmeans algorithm is used to process the unlabeled points to obtain initial labels. Then, effective label points are selected and added to the existing labeled points to form a new labeled training set. Finally, SVM is combined to classify the unlabeled points. |
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