A novel supervised classifier for remote sensing data by employing clonal selection algorithm(CSA)is presented for solving the problems,e. g. local optimum and robustness,in remote sensing imagery classification.
针对遥感影像分类中的局部极值、鲁棒性等问题,提出基于克隆选择算法的遥感影像监督分类方法。
参考来源 - 克隆选择算法在遥感影像分类中的应用·2,447,543篇论文数据，部分数据来源于NoteExpress
Using pseudowords we can overcome data sparseness problem in supervised WSD and fully verify the experimental effect of word sense classifier.
使用伪词可以避免有指导的词义消歧方法中的数据稀疏问题，充分验证词义分类器的实验效果。
However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.
然而，传统的监督学习算法需要标记大量的训练样本来建立满意的分类器。
So, the semi-supervised learning method by learning a small number of labeling samples and a large number of samples to establish classifier came into being.
如此，通过对少量已标记样本和大量未标记的样本进行学习从而建立分类器的半监督学习方法应运而生。
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