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dc.contributor.advisor谭忠
dc.contributor.author陈欣
dc.date.accessioned2018-12-05T01:40:28Z
dc.date.available2018-12-05T01:40:28Z
dc.date.issued2017-12-27
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/170079
dc.description.abstract随着大数据时代的来临,挖掘数据潜在的价值成为了各领域学者、工作者致力于研究的课题之一。文本数据承载着大量的信息,垃圾文本分类作为文本挖掘中经典的课题之一,虽然已经有了长足的发展,但提升垃圾文本分类器的精度一直都是人们追求的目标。BP神经网络模型是一种非常有效的非线性模型,它通过模拟生物神经网络,可以较好的拟合线性不可分的数据,是进行分类问题的常用模型之一。现今,文本数据呈爆炸式增长,呈现数据量大、高纬度、“有标签”数据少“未标签”数据多等特点,传统的BP神经网络已不能很好地解决这些问题。本篇论文中,我采用改进后的BP神经网络进行改进,并结合基于图的半监督学习方法在垃圾文本分类问题上进行实证分析...
dc.description.abstractWith the advent of the era of big data, mining the potential value of data has become one of the popular research topics. Text data is loaded with a large amount of information, although spam text classification is well developed, which is one of the most well-known subjects of text mining, improving the accuracy of the spam text classifier has been a goal pursued by people. BP neural network is a...
dc.language.isozh_CN
dc.relation.urihttps://catalog.xmu.edu.cn/opac/openlink.php?strText=58275&doctype=ALL&strSearchType=callno
dc.source.urihttps://etd.xmu.edu.cn/detail.asp?serial=60728
dc.subjectBP神经网络
dc.subjectElastic Net
dc.subject基于图的半监督
dc.subjectBP neural network
dc.subjectElastic Net
dc.subjectgraph based semi-supervised
dc.title基于半监督的带Elastic Net正则项BP神经网络在文本分类上的应用
dc.title.alternativeImproved BP neural network combined with semi-supervised algorithm and its application on text classification
dc.typethesis
dc.date.replied2017-05-26
dc.description.note学位:理学硕士
dc.description.note院系专业:数学科学学院_应用数学
dc.description.note学号:19020141152625


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