Many existing data mining and machine learning techniques fail when training and test data have different distributions or feature spaces.
众所周知,当训练数据和测试数据的分布或特征空间不同时,许多数据挖掘和机器学习技术很有可能会失败。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
目前,离群挖掘正逐渐成为数据库、机器学习、统计学等领域研究人员的研究热点。
Machine learning and data mining techniques are applied to acquire knowledge and build a concept reasoning network based on semantic dictionary and large training set.
在已有的英语语义词典及大量训练集的基础上,应用机器学习、数据挖掘等技术进行知识获取并最终形成若干个概念推理网。
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