Improving Supervised Learning with Multiple Clusterings
نویسندگان
چکیده
Classification task involves inducing a predictive model using a set of labeled samples. The more the labeled samples are, the better the model is. When one has only a few samples, the obtained model tends to offer poor result. Even when labeled samples are difficult to get, a lot of unlabeled samples are generally available on which unsupervised learning can be used. In this paper, a way to combine supervised and unsupervised learning in order to use both labeled and unlabeled samples is explored. The efficiency of the method is evaluated on various UCI datasets when the number of labeled samples is very low.
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تاریخ انتشار 2009