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中文摘要：
利用构造性学习（CML）算法训练分类器需要大量已标记样本,然而获取大量已标记的样本较为困难。为此,提出了一种协同半监督的构造性学习算法。将已标记样本等分为三个训练集,分别使用构造性学习算法训练三个单分类器,以共同投票的方式对未标记样本进行标记,从而依次扩充三个单分类器训练集直到不能再扩充为止。将三个训练集合并训练出最终的分类器。选取UCI数据集进行实验,结果表明,与CML算法、Tri-CML算法、NB算法及Tri-NB相比,该方法的分类更为有效。
英文摘要：
Constructive Machine Learning（CML）algorithm needs larger number of labeled samples to train a classification network, but it is difficult to obtain a mass of labeled samples. So a constructive learning method based on co-training algorithm is designed. It divides the limited labeled samples into three equal training sets and uses CML algorithm to train three single classifiers respectively. The unlabeled samples are labeled by voting together through the three single classifiers to expand the three training sets in turn until they are not expanded. The expanded three training sets are united to generate the final classifier. Experiment is conducted on UCI data set and results show that the algorithm is more effective than the CML, Tri-CML, NB and Tri-NB algorithms.
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