semisup-learn:python的半监督学习框架,允许将scikit-learn分类器拟合到部分标记的数据-源码

上传者: 42116604 | 上传时间: 2021-09-08 14:52:30 | 文件大小: 931KB | 文件类型: ZIP
Python的半监督学习框架 该项目包含用于半监督学习的Python实现,并与scikit-learn兼容,包括 对比悲观似然估计(CPLE) (基于-但不等同于 ),适用于所有分类器的“安全”框架,可以产生预测概率(此处的安全意味着在标签和未标签数据上训练的模型都应不会比仅根据标签数据训练的模型差) 自我学习(自我训练),一种适用于任何分类器的幼稚半监督学习框架(使用经过训练的分类器迭代标记未标记的实例,然后在结果数据集上对其进行重新训练-参见例如 ) 半监督支持向量机(S3VM)-一种简单的scikit-learn兼容包装器,用于由Fabian Gieseke,Antti Airola,Tapio Pahikkala,Oliver Kramer开发的QN-S3VM代码(请参见 )包含此方法用于比较 第一种方法是对的新颖扩展用于任何区分性分类器(与原始CPLE的区别在下面进行说明)。

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