Invention Grant
US09275347B1 Online content classifier which updates a classification score based on a count of labeled data classified by machine deep learning
有权
在线内容分类器,其基于通过机器深度学习分类的标记数据的计数来更新分类分数
- Patent Title: Online content classifier which updates a classification score based on a count of labeled data classified by machine deep learning
- Patent Title (中): 在线内容分类器,其基于通过机器深度学习分类的标记数据的计数来更新分类分数
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Application No.: US14879787Application Date: 2015-10-09
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Publication No.: US09275347B1Publication Date: 2016-03-01
- Inventor: Hitoshi Harada , Yuki Hayashi
- Applicant: AlpacaDB, Inc.
- Applicant Address: US CA San Mateo
- Assignee: AlpacaDB, Inc.
- Current Assignee: AlpacaDB, Inc.
- Current Assignee Address: US CA San Mateo
- Agency: Lowe Graham Jones PLLC
- Agent John W. Branch
- Main IPC: G06F15/18
- IPC: G06F15/18 ; G06N99/00 ; G06F17/30 ; G10L15/08

Abstract:
Embodiments are directed towards online content classification that includes training a machine learning system. A batch of data items may be randomly selected from unlabeled test data. The batch of data items may be communicated to a client computer enabling a user to label each data item based on the contents of each data item. These labeled data items may be employed to train the machine learning system. While a classification result score is less than a threshold value, iteration may be performed to train the machine learning system. For each iteration another batch of data items may be selected from the unlabeled test data. This batch of data items may be classified using the machine learning system. The batch of classified data items may be communicated back to the client computer to be labeled by the user.
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