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公开(公告)号：US20080270329A1
公开(公告)日：2008-10-30
申请号：US12045458
申请日：2008-03-10
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06K9/6256
摘要： Boosting algorithms are provided for accelerated machine learning in the presence of misclassification noise. In an exemplary embodiment, a machine learning method having multiple learning stages is provided. Each learning stage may include partitioning examples into bins, choosing a base classifier for each bin, and assigning an example to a bin by counting the number of positive predictions previously made by the base classifier associated with the bin.
摘要翻译： 提供增强算法用于在存在错误分类噪声的情况下加速机器学习。 在一个示例性实施例中，提供了具有多个学习阶段的机器学习方法。 每个学习阶段可以包括将示例分割成分区，为每个分组选择一个基本分类器，并且通过对与该分组相关联的基本分类器先前做出的肯定预测的数量进行计数，将一个示例分配给一个分组。
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公开(公告)号：US08036996B2
公开(公告)日：2011-10-11
申请号：US12045458
申请日：2008-03-10
CPC分类号： G06N99/005 , G06K9/6256
摘要： Boosting algorithms are provided for accelerated machine learning in the presence of misclassification noise. In an exemplary embodiment, a machine learning method having multiple learning stages is provided. Each learning stage may include partitioning examples into bins, choosing a base classifier for each bin, and assigning an example to a bin by counting the number of positive predictions previously made by the base classifier associated with the bin.
摘要翻译： 提供增强算法用于在存在错误分类噪声的情况下加速机器学习。 在一个示例性实施例中，提供了具有多个学习阶段的机器学习方法。 每个学习阶段可以包括将示例分割成分区，为每个分组选择一个基本分类器，并且通过对与该分组相关联的基本分类器先前做出的肯定预测的数量进行计数，将一个示例分配给一个分组。
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