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1. 发明授权

US08694444B2 Learning multiple tasks with boosted decision trees 有权
标题翻译：用强大的决策树学习多个任务
公开(公告)号：US08694444B2
公开(公告)日：2014-04-08
申请号：US13451816
申请日：2012-04-20
申请人： Jean-Baptiste Faddoul , Boris Chidlovskii
发明人： Jean-Baptiste Faddoul , Boris Chidlovskii
IPC分类号： G06N5/00
CPC分类号： H04L51/12
摘要： A multi-task machine learning method is performed to generate a multi-task (MT) predictor for a set of tasks including at least two tasks. The machine learning method includes: learning a multi-task decision tree (MT-DT) including learning decision rules for nodes of the MT-DT that optimize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks; and constructing the MT predictor based on the learned MT-DT. In some embodiments the aggregate IG is the largest single-task IG value of the single-task IG values. In some embodiments the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's.
摘要翻译：执行多任务机器学习方法以为包括至少两个任务的一组任务生成多任务（MT）预测器。机器学习方法包括：学习包括MT-DT节点的学习决策规则的多任务决策树（MT-DT），所述学习决策规则优化聚集组合任务IG值的集合信息增益（IG），用于集合的任务的任务并基于学习的MT-DT构建MT预测器。在一些实施例中，聚合IG是单个任务IG值的最大单个任务IG值。在一些实施例中，机器学习方法包括重复用于训练集合的不同子集的MT-DT学习操作以生成一组学习MT-DT，并且构造包括将MT预测器构造为该组的输出的加权组合 MT-DT。

2. 发明授权

US08954357B2 Multi-task machine learning using features bagging and local relatedness in the instance space 有权
标题翻译：多任务机器学习使用功能包装和局部相关性在实例空间
公开(公告)号：US08954357B2
公开(公告)日：2015-02-10
申请号：US13106105
申请日：2011-05-12
申请人： Jean-Baptiste Faddoul , Boris Chidlovskii
发明人： Jean-Baptiste Faddoul , Boris Chidlovskii
IPC分类号： G06F15/18 , G06N99/00
CPC分类号： G06N99/005
摘要： A multi-task machine learning component learns a set of tasks comprising two or more different tasks based on a set of examples. The examples are represented by features of a set of features. The multi-task machine learning component comprises a digital processing device configured to learn an ensemble of base rules wherein each base rule is learned for a sub-set of the set of features and comprises a multi-task decision tree (MT-DT) having nodes comprising decision rules for tasks of the set of tasks. An inference component comprises a digital processing device configured to predict a result for at least one task of the set of tasks for an input item represented by features of the set of features using the learned ensemble of base rules.
摘要翻译：多任务机器学习组件基于一组示例学习包括两个或多个不同任务的一组任务。这些例子由一组特征的特征表示。多任务机器学习部件包括被配置为学习基本规则的集合的数字处理设备，其中为该特征集合的子集学习每个基本规则，并且包括具有多任务决策树（MT-DT）的多任务决策树（MT-DT），其具有节点包括用于该组任务的任务的决策规则。推理组件包括数字处理设备，该数字处理设备被配置为使用所学习的基本规则的集合来预测由所述特征集合的特征所表示的输入项目的所述任务集合中的至少一个任务的结果。

3. 发明申请

US20130282627A1 LEARNING MULTIPLE TASKS WITH BOOSTED DECISION TREES 有权
标题翻译：学习具有强化决策权的多种任务
公开(公告)号：US20130282627A1
公开(公告)日：2013-10-24
申请号：US13451816
申请日：2012-04-20
申请人： Jean-Baptiste Faddoul , Boris Chidlovskii
发明人： Jean-Baptiste Faddoul , Boris Chidlovskii
IPC分类号： G06F15/18
CPC分类号： H04L51/12
摘要： A multi-task machine learning method is performed to generate a multi-task (MT) predictor for a set of tasks including at least two tasks. The machine learning method includes: learning a multi-task decision tree (MT-DT) including learning decision rules for nodes of the MT-DT that optimize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks; and constructing the MT predictor based on the learned MT-DT. In some embodiments the aggregate IG is the largest single-task IG value of the single-task IG values. In some embodiments the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's.
摘要翻译：执行多任务机器学习方法以为包括至少两个任务的一组任务生成多任务（MT）预测器。机器学习方法包括：学习包括MT-DT节点的学习决策规则的多任务决策树（MT-DT），所述学习决策规则优化聚集组合任务IG值的集合信息增益（IG），用于集合的任务的任务并基于学习的MT-DT构建MT预测器。在一些实施例中，聚合IG是单个任务IG值的最大单个任务IG值。在一些实施例中，机器学习方法包括重复用于训练集合的不同子集的MT-DT学习操作以生成一组学习的MT-DT，并且构造包括将MT预测器构造为该组的输出的加权组合 MT-DT。

4. 发明申请

US20120290510A1 MULTI-TASK MACHINE LEARNING USING FEATURES BAGGING AND LOCAL RELATEDNESS IN THE INSTANCE SPACE 有权
标题翻译：使用特征的多任务机器学习在实际空间中的包围和本地相关性
公开(公告)号：US20120290510A1
公开(公告)日：2012-11-15
申请号：US13106105
申请日：2011-05-12
申请人： Jean-Baptiste Faddoul , Boris Chidlovskii
发明人： Jean-Baptiste Faddoul , Boris Chidlovskii
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： A multi-task machine learning component learns a set of tasks comprising two or more different tasks based on a set of examples. The examples are represented by features of a set of features. The multi-task machine learning component comprises a digital processing device configured to learn an ensemble of base rules wherein each base rule is learned for a sub-set of the set of features and comprises a multi-task decision tree (MT-DT) having nodes comprising decision rules for tasks of the set of tasks. An inference component comprises a digital processing device configured to predict a result for at least one task of the set of tasks for an input item represented by features of the set of features using the learned ensemble of base rules.
摘要翻译：多任务机器学习组件基于一组示例学习包括两个或多个不同任务的一组任务。这些例子由一组特征的特征表示。多任务机器学习部件包括被配置为学习基本规则的集合的数字处理设备，其中为该特征集合的子集学习每个基本规则，并且包括具有多任务决策树（MT-DT）的多任务决策树（MT-DT），其具有节点包括用于该组任务的任务的决策规则。推理组件包括数字处理设备，该数字处理设备被配置为使用所学习的基本规则的集合来预测由所述特征集合的特征所表示的输入项目的所述任务集合中的至少一个任务的结果。

5. 发明授权

US08280718B2 Method to preserve the place of parentheses and tags in statistical machine translation systems 有权
标题翻译：在统计机器翻译系统中保留括号和标签的位置的方法
公开(公告)号：US08280718B2
公开(公告)日：2012-10-02
申请号：US12404412
申请日：2009-03-16
申请人： Jean Baptiste Faddoul , Francois Pacull
发明人： Jean Baptiste Faddoul , Francois Pacull
IPC分类号： G06F17/28
CPC分类号： G06F17/2818
摘要： An apparatus, method, and a computer program product are provided for improving the translation quality of text when a text sequence, such as a sentence, includes a parenthetical part delimited by a separator or separators, such as parentheses, quotation marks, or the like. The apparatus applies an algorithm which is configured for using several methodologies in sequence to identify an optimum position for insertion of the translated parenthetical in the main part of the translated text sequence. As a result, the translated parenthetical is more likely to be correctly delimited by separators and more likely to be correctly placed in the main part than in a conventional translation system which automatically translates each sentence as a whole.
摘要翻译：提供了一种装置，方法和计算机程序产品，用于当诸如句子的文本序列包括由分隔符或分隔符（例如括号，引号等）分隔的括号部分时，提高文本的翻译质量。该装置应用一种算法，其被配置为按顺序使用多种方法来识别在翻译的文本序列的主要部分中插入翻译的括号的最佳位置。因此，翻译的括号更有可能被分隔符正确分隔，更有可能正确放置在主要部分，而不是自动翻译每个句子作为一个整体的常规翻译系统。

6. 发明申请

US20100235162A1 METHOD TO PRESERVE THE PLACE OF PARENTHESES AND TAGS IN STATISTICAL MACHINE TRANSLATION SYSTEMS 有权
标题翻译：保存统计机器翻译系统中父母和标签位置的方法
公开(公告)号：US20100235162A1
公开(公告)日：2010-09-16
申请号：US12404412
申请日：2009-03-16
申请人： Jean Baptiste FADDOUL , Francois Pacull
发明人： Jean Baptiste FADDOUL , Francois Pacull
IPC分类号： G06F17/28
CPC分类号： G06F17/2818
摘要： An apparatus, method, and a computer program product are provided for improving the translation quality of text when a text sequence, such as a sentence, includes a parenthetical part delimited by a separator or separators, such as parentheses, quotation marks, or the like. The apparatus applies an algorithm which is configured for using several methodologies in sequence to identify an optimum position for insertion of the translated parenthetical in the main part of the translated text sequence. As a result, the translated parenthetical is more likely to be correctly delimited by separators and more likely to be correctly placed in the main part than in a conventional translation system which automatically translates each sentence as a whole.
摘要翻译：提供了一种装置，方法和计算机程序产品，用于当诸如句子的文本序列包括由分隔符或分隔符（例如括号，引号等）分隔的括号部分时，提高文本的翻译质量。该装置应用一种算法，其被配置为按顺序使用多种方法来识别在翻译的文本序列的主要部分中插入翻译的括号的最佳位置。因此，翻译的括号更有可能被分隔符正确分隔，更有可能正确放置在主要部分，而不是自动翻译每个句子作为一个整体的常规翻译系统。

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