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1. 发明申请

US20140095493A1 DOCUMENT RELEVANCY ANALYSIS WITHIN MACHINE LEARNING SYSTEMS 审中-公开
标题翻译：在机器学习系统中的文档相关分析
公开(公告)号：US20140095493A1
公开(公告)日：2014-04-03
申请号：US13952501
申请日：2013-07-26
申请人： Christian Feuersänger , Dietrich Wettschereck , Jan Puzicha
发明人： Christian Feuersänger , Dietrich Wettschereck , Jan Puzicha
IPC分类号： G06F17/30
CPC分类号： G06F17/3053 , G06F17/30687 , G06F17/30705 , G06N99/005
摘要： Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification.
摘要翻译：本文提供了量化文档相对于训练语料库的文档相关性并选择最佳匹配或最佳匹配的系统和方法。方法可以包括通过执行支持向量机分类器来产生对文档与训练语料库的相关性的基于示例的解释，所述支持向量机分类器在术语频率逆文档频率特征空间相对位置中执行相关文档的质心分类在训练语料库中训练示例，并且基于质心分类从训练示例中选择相关文档的最佳匹配来生成基于示例的解释。

2. 发明授权

US08533148B1 Document relevancy analysis within machine learning systems including determining closest cosine distances of training examples 有权
标题翻译：机器学习系统中的文档相关性分析，包括确定训练样本的最近余弦距离
公开(公告)号：US08533148B1
公开(公告)日：2013-09-10
申请号：US13632943
申请日：2012-10-01
申请人： Christian Feuersänger , Dietrich Wettschereck , Jan Puzicha
发明人： Christian Feuersänger , Dietrich Wettschereck , Jan Puzicha
IPC分类号： G06F15/00 , G06F15/18
CPC分类号： G06F17/3053 , G06F17/30687 , G06F17/30705 , G06N99/005
摘要： Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification. Determining the training example having the closest cosine distance to the relevant document includes ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval.
摘要翻译：本文提供了量化文档相对于训练语料库的文档相关性并选择最佳匹配或最佳匹配的系统和方法。方法可以包括通过执行支持向量机分类器来产生对文档与训练语料库的相关性的基于示例的解释，所述支持向量机分类器在术语频率逆文档频率特征空间相对位置中执行相关文档的质心分类在训练语料库中训练示例，并且基于质心分类从训练示例中选择相关文档的最佳匹配来生成基于示例的解释。确定具有与相关文档最近的余弦距离的训练示例包括通过线性地拉伸训练示例的内部最佳匹配分数来覆盖训练样本来对整个单位间隔进行排序。

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