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公开(公告)号：US09378464B2
公开(公告)日：2016-06-28
申请号：US13561318
申请日：2012-07-30
CPC分类号： G06N99/005 , G10L15/08
摘要： A system and an article of manufacture for discriminative learning via hierarchical transformations, which includes obtaining a model of a first set of data, two or more data transformations, and a second set of data, evaluating the two or more data transformations to determine which data transformation will most effectively modify the second set of data to match the model, and selecting the data transformation that will most effectively modify the second set of data to match the model based on the evaluation.
摘要翻译： 一种用于通过分级变换进行鉴别学习的系统和制品，其包括获得第一组数据，两个或多个数据变换和第二组数据的模型，评估两个或多个数据变换以确定哪些数据 转换将最有效地修改第二组数据以匹配模型，并选择最有效地修改第二组数据的数据转换，以基于评估来匹配模型。
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公开(公告)号：US20140032571A1
公开(公告)日：2014-01-30
申请号：US13561318
申请日：2012-07-30
IPC分类号： G06F17/30
CPC分类号： G06N99/005 , G10L15/08
摘要： A system and an article of manufacture for discriminative learning via hierarchical transformations, which includes obtaining a model of a first set of data, two or more data transformations, and a second set of data, evaluating the two or more data transformations to determine which data transformation will most effectively modify the second set of data to match the model, and selecting the data transformation that will most effectively modify the second set of data to match the model based on the evaluation.
摘要翻译： 一种用于通过分级变换进行鉴别学习的系统和制品，其包括获得第一组数据，两个或多个数据变换和第二组数据的模型，评估两个或多个数据变换以确定哪些数据 转换将最有效地修改第二组数据以匹配模型，并选择最有效地修改第二组数据的数据转换，以基于评估来匹配模型。
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公开(公告)号：US20140032570A1
公开(公告)日：2014-01-30
申请号：US13561148
申请日：2012-07-30
IPC分类号： G06F17/30
摘要： Techniques for discriminative learning via hierarchical transformations. A method includes obtaining a model of a first set of data, two or more data transformations, and a second set of data, evaluating the two or more data transformations to determine which data transformation will most effectively modify the second set of data to match the model, and selecting the data transformation that will most effectively modify the second set of data to match the model based on the evaluation.
摘要翻译： 通过层次转换进行歧视性学习的技术。 一种方法包括获得第一组数据，两个或多个数据变换和第二组数据的模型，评估两个或多个数据变换以确定哪个数据变换将最有效地修改第二组数据以匹配 模型，并选择最有效地修改第二组数据的数据转换，以基于评估来匹配模型。
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公开(公告)号：US20140164299A1
公开(公告)日：2014-06-12
申请号：US13707088
申请日：2012-12-06
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： Pretraining for a DBN initializes weights of the DBN (Deep Belief Network) using a hybrid pre-training methodology. Hybrid pre-training employs generative component that allows the hybrid PT method to have better performance in WER (Word Error Rate) compared to the discriminative PT method. Hybrid pre-training learns weights which are more closely linked to the final objective function, allowing for a much larger batch size compared to generative PT, which allows for improvements in speed; and a larger batch size allows for parallelization of the gradient computation, speeding up training further.
摘要翻译： 预先训练DBN使用混合预训练方法初始化DBN（深信仰网络）的权重。 混合预训练采用生成部件，与辨别性PT方法相比，允许混合PT方法在WER（字错误率）方面具有更好的性能。 混合预训练学习与最终目标函数更紧密相关的权重，允许与生成PT相比更大的批量大小，这允许提高速度; 并且较大的批量允许梯度计算的并行化，进一步加速训练。
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公开(公告)号：US08311848B2
公开(公告)日：2012-11-13
申请号：US12861563
申请日：2010-08-23
申请人： Muthiah Subash , Ilesh Kurani , Brian Kingsbury
发明人： Muthiah Subash , Ilesh Kurani , Brian Kingsbury
IPC分类号： G06Q50/00
摘要： An electronic medical record system enables organic growth of medical conditions or choices linked to predefined human body systems or an anatomical image. These choices are used as blue print launchers that form sentences/paragraphs as a user interacts with the choice lists. The choice lists can include a set of control variables that can be selected to create a medical history of a patient in the form of one or more visit notes. The medical history of the patient is formed by user selection of some of the control variables for insertion into fillable data entry locations in the sentences/paragraphs. The sentences/paragraphs are in the form of blue prints which are available generically to be populated with patient specific information to form customized blue prints for a particular patient. Multiple data perspectives in the electronic medical record system allows relatively easy and simultaneous collection of the complex medical data to produce medical visit notes or letters during a patient office visit.
摘要翻译： 电子医疗记录系统能够有机地增长与预定义的人体系统或解剖图像相关联的医疗条件或选择。 这些选择被用作蓝色打印发射器，它们在用户与选择列表进行交互时形成句子/段落。 选择列表可以包括可以选择以一个或多个访问笔记的形式创建患者病历的一组控制变量。 通过用户选择用于插入到句子/段落中的可填充数据输入位置的一些控制变量形成病人的病史。 句子/段落是蓝色打印的形式，其通常可用于填充患者特定信息以形成针对特定患者的定制蓝色打印。 电子医疗记录系统中的多个数据视角允许相对容易和同时收集复杂的医疗数据以在病人访问期间产生医疗访问笔记或信件。
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公开(公告)号：US20110082710A1
公开(公告)日：2011-04-07
申请号：US12861563
申请日：2010-08-23
申请人： Muthiah Subash , Ilesh Kurani , Brian Kingsbury
发明人： Muthiah Subash , Ilesh Kurani , Brian Kingsbury
IPC分类号： G06Q50/00
摘要： An electronic medical record system enables organic growth of medical conditions or choices linked to predefined human body systems or an anatomical image. These choices are used as blue print launchers that form sentences/paragraphs as a user interacts with the choice lists. The choice lists can include a set of control variables that can be selected to create a medical history of a patient in the form of one or more visit notes. The medical history of the patient is formed by user selection of some of the control variables for insertion into fillable data entry locations in the sentences/paragraphs. The sentences/paragraphs are in the form of blue prints which are available generically to be populated with patient specific information to form customized blue prints for a particular patient. Multiple data perspectives in the electronic medical record system allows relatively easy and simultaneous collection of the complex medical data to produce medical visit notes or letters during a patient office visit.
摘要翻译： 电子医疗记录系统能够有机地增长与预定义的人体系统或解剖图像相关联的医疗条件或选择。 这些选择被用作蓝色打印发射器，它们在用户与选择列表进行交互时形成句子/段落。 选择列表可以包括可以选择以一个或多个访问笔记的形式创建患者病历的一组控制变量。 通过用户选择用于插入到句子/段落中的可填充数据输入位置的一些控制变量形成病人的病史。 句子/段落是蓝色打印的形式，其通常可用于填充患者特定信息以形成针对特定患者的定制蓝色打印。 电子医疗记录系统中的多个数据视角允许相对容易和同时收集复杂的医疗数据以在病人访问期间产生医疗访问笔记或信件。
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