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公开(公告)号：US09141916B1
公开(公告)日：2015-09-22
申请号：US13803779
申请日：2013-03-14
申请人： Google Inc.
发明人： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Gary R. Holt , Julian P. Grady , Sharat Chikkerur , David W. Sculley
CPC分类号： G06N3/08 , G06N3/04 , G06N3/0454 , G06N3/084
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.
摘要翻译： 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用具有深度网络的嵌入式功能。 方法之一包括接收包括多个特征的输入，其中每个特征具有不同的特征类型; 使用相应的嵌入功能处理每个特征以生成一个或多个数值，其中每个嵌入功能独立于彼此嵌入功能操作，并且其中每个嵌入功能用于相应特征类型的特征; 使用深度网络处理所述数值以产生所述输入的第一替代表示，其中所述深度网络是由多个非线性操作级别组成的机器学习模型; 以及使用逻辑回归分类器处理输入的第一替代表示以预测输入的标签。
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公开(公告)号：US09514404B1
公开(公告)日：2016-12-06
申请号：US14860497
申请日：2015-09-21
申请人： Google Inc.
发明人： Gregory S. Corrado , Kai Chen , Jeffrey A. Dean , Gary R. Holt , Julian P. Grady , Sharat Chikkerur , David W. Sculley, II
CPC分类号： G06N3/08 , G06N3/04 , G06N3/0454 , G06N3/084
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using embedded function with a deep network. One of the methods includes receiving an input comprising a plurality of features, wherein each of the features is of a different feature type; processing each of the features using a respective embedding function to generate one or more numeric values, wherein each of the embedding functions operates independently of each other embedding function, and wherein each of the embedding functions is used for features of a respective feature type; processing the numeric values using a deep network to generate a first alternative representation of the input, wherein the deep network is a machine learning model composed of a plurality of levels of non-linear operations; and processing the first alternative representation of the input using a logistic regression classifier to predict a label for the input.
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