公开(公告)号：US20150186794A1

公开(公告)日：2015-07-02

申请号：US14142970

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie ,  James Vincent McFadden ,  Jeremiah Harmsen ,  Kristen Riedt LeFevre

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract translation: 公开了用于基于与一个或多个特征相关联的一个或多个正则化惩罚来训练机器学习模型的系统和技术。 具有较低正则化罚分的模板可以优先于具有较高正则化惩罚的模板。 正规化惩罚可以根据领域知识来确定。 基于确定模板出现低于稳定性阈值，可以将限制性正则化惩罚分配给模板，并且如果模板发生满足或超过稳定性阈值，则可以对模板进行修改。

公开(公告)号：US09390382B2

公开(公告)日：2016-07-12

申请号：US14142970

申请日：2013-12-30

Applicant: Google Inc.

Inventor： Yoram Singer ,  Tal Shaked ,  Tushar Deepak Chandra ,  Tze Way Eugene Ie ,  James Vincent McFadden ,  Jeremiah Harmsen ,  Kristen Riedt LeFevre

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: Systems and techniques are disclosed for training a machine learning model based on one or more regularization penalties associated with one or more features. A template having a lower regularization penalty may be given preference over a template having a higher regularization penalty. A regularization penalty may be determined based on domain knowledge. A restrictive regularization penalty may be assigned to a template based on determining that a template occurrence is below a stability threshold and may be modified if the template occurrence meets or exceeds the stability threshold.

Abstract translation: 公开了用于基于与一个或多个特征相关联的一个或多个正则化惩罚来训练机器学习模型的系统和技术。 具有较低正则化罚分的模板可以优先于具有较高正则化惩罚的模板。 正规化惩罚可以根据领域知识来确定。 基于确定模板出现低于稳定性阈值，可以将限制性正则化惩罚分配给模板，并且如果模板发生满足或超过稳定性阈值，则可以对模板进行修改。