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公开(公告)号：US11017272B2
公开(公告)日：2021-05-25
申请号：US15827294
申请日：2017-11-30
Applicant: Facebook, Inc.
Inventor： Jianfu Chen , Timothy Jacoby
Abstract: An online system actively and randomly selects content items to be labeled for training a classifier. An online system receives content items from client devices of users and selects sets of the content items to be labeled by human labelers. The randomly selected content items are selected at random from the received content items, and the actively selected content items are selected based on the classifier's confidence in accurately predicting the classification of the content items. The online system may use a histogram of content items to actively select content items. The online system assigns the content items to bins of the histogram based on priority scores and selects content items with priority scores of the highest percentile. The online system provides the selected content items to human labelers for labeling. The labeled content items are then used for training the classifier.
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公开(公告)号：US20190164017A1
公开(公告)日：2019-05-30
申请号：US15827294
申请日：2017-11-30
Applicant: Facebook, Inc.
Inventor： Jianfu Chen , Timothy Jacoby
Abstract: An online system actively and randomly selects content items to be labeled for training a classifier. An online system receives content items from client devices of users and selects sets of the content items to be labeled by human labelers. The randomly selected content items are selected at random from the received content items, and the actively selected content items are selected based on the classifier's confidence in accurately predicting the classification of the content items. The online system may use a histogram of content items to actively select content items. The online system assigns the content items to bins of the histogram based on priority scores and selects content items with priority scores of the highest percentile. The online system provides the selected content items to human labelers for labeling. The labeled content items are then used for training the classifier.
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公开(公告)号：US20180276561A1
公开(公告)日：2018-09-27
申请号：US15469399
申请日：2017-03-24
Applicant: Facebook, Inc.
Inventor： Jeffrey William Pasternack , David Vickrey , Justin MacLean Coughlin , Prasoon Mishra , Austen Norment McDonald , Max Christian Eulenstein , Jianfu Chen , Kritarth Anand , Polina Kuznetsova
CPC classification number: G06N20/00 , G06F16/353 , G06N5/041 , G06N20/20
Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.
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公开(公告)号：US10740690B2
公开(公告)日：2020-08-11
申请号：US15469399
申请日：2017-03-24
Applicant: Facebook, Inc.
Inventor： Jeffrey William Pasternack , David Vickrey , Justin MacLean Coughlin , Prasoon Mishra , Austen Norment McDonald , Max Christian Eulenstein , Jianfu Chen , Kritarth Anand , Polina Kuznetsova
Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.
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