Both theories are combined to classify the documents by unsupervised learning and discuss the method in which new rules , applied to new unclassified documents , can be formed after classifying the training documents 本文利用文档聚类和粗糙集约简相结合的方法,对训练文档进行分类,形成规则后对新加入的未分类文档进行归类。
3 ) semantic classification model based som network we use the classification model to combines attributes within a database . this is done using an unsupervised learning algorithm . the output is used as training data for the next stage 3 )基于som网络的语义分类模型设计建立som网络模型,将元数据特征向量进行分类,形成bp网络的目标向量,用于匹配规则的提取。
In machine learning, unsupervised learning refers to the problem of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution.