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1. 发明授权

US07761391B2 Methods and systems for improved transductive maximum entropy discrimination classification 有权
标题翻译：用于改进转换最大熵辨别分类的方法和系统
公开(公告)号：US07761391B2
公开(公告)日：2010-07-20
申请号：US11752634
申请日：2007-05-23
申请人： Mauritius A. R. Schmidtler , Christopher K. Harris
发明人： Mauritius A. R. Schmidtler , Christopher K. Harris
IPC分类号： G06N5/00
CPC分类号： G06N99/005
摘要： A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof.
摘要翻译：提供了一种用于对数据进行分类的系统，方法，数据处理装置和制品。标签数据点被接收，每个标记数据点具有至少一个标签，指示数据点是否是用于包括在指定类别中的数据点的训练示例，或者是从指定类别排除的数据点的训练示例; 接收未标记的数据点; 接收标记数据点和未标记数据点的至少一个预定成本因子; 通过使用至少一个成本因子和标记的数据点和未标记的数据点作为训练示例的迭代计算来训练使用MED的转换分类器; 应用经过训练的分类器对未标记的数据点，标记数据点和输入数据点中的至少一个进行分类; 并输出分类数据点或其派生物的分类。

2. 发明授权

US07533076B2 Effective multi-class support vector machine classification 有权
标题翻译：有效的多类支持向量机分类
公开(公告)号：US07533076B2
公开(公告)日：2009-05-12
申请号：US12050096
申请日：2008-03-17
申请人： Christopher K. Harris , Mauritius A. R. Schmidtler
发明人： Christopher K. Harris , Mauritius A. R. Schmidtler
IPC分类号： G06F17/00 , G06N7/00 , G06F15/18 , G06N7/08 , G06N5/00
CPC分类号： G06K9/6269
摘要： An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm. In one preferred embodiment, the method includes the following steps: storing a plurality of user-defined categories in a memory of a computer, analyzing a plurality of training examples for each category so as to identify one or more features associated with each category; calculating at least one feature vector for each of the examples; transforming each of the at least one feature vectors so as reflect information about all of the training examples; and building a SVM classifier for each one of the plurality of categories, wherein the process of building a SVM classifier further includes: assigning each of the examples in a first category to a first class and all other examples belonging to other categories to a second class, wherein if anyone of the examples belongs to another category as well as the first category, such examples are assigned to the first class only, optimizing at least one tunable parameter of a SVM classifier for the first category, wherein the SVM classifier is trained using the first and second classes; and optimizing a function that converts the output of the binary SVM classifier into a probability of category membership.
摘要翻译：一种使用二进制支持向量机（SVM）算法将示例分类为多个类别的改进方法。在一个优选实施例中，该方法包括以下步骤：将多个用户定义的类别存储在计算机的存储器中，分析每个类别的多个训练示例，以便识别与每个类别相关联的一个或多个特征; 为每个示例计算至少一个特征向量; 转换所述至少一个特征向量中的每一个，以便反映关于所有训练示例的信息; 以及为所述多个类别中的每个类别构建SVM分类器，其中，构建SVM分类器的过程还包括：将第一类别中的每个示例分配给第一类，将属于其他类别的所有其他示例分配给第二类其中如果任何示例属于另一类别以及第一类别，则将这些示例仅分配给第一类，优化用于第一类别的SVM分类器的至少一个可调参数，其中，SVM分类器使用第一类和第二类; 并优化将二进制SVM分类器的输出转换成类别成员的概率的函数。

3. 发明授权

US08693043B2 Automatic document separation 有权
标题翻译：自动文档分离
公开(公告)号：US08693043B2
公开(公告)日：2014-04-08
申请号：US10742131
申请日：2003-12-19
申请人： Mauritius A. R. Schmidtler , Scott S. Texeira , Christopher K. Harris , Sameer Samat , Roland Borrey , Anthony Macciola
发明人： Mauritius A. R. Schmidtler , Scott S. Texeira , Christopher K. Harris , Sameer Samat , Roland Borrey , Anthony Macciola
IPC分类号： G06K15/00
CPC分类号： G06F17/21 , G06K9/00442 , H04N1/32112 , H04N2201/3225 , H04N2201/3243
摘要： A method and system for delineating document boundaries and identifying document types by analyzing digital images of one or more documents, automatically categorizing one or more pages or subdocuments within the one or more documents and automatically generating delineation identifiers, such as computer-generated images of separation pages inserted between digital images belonging to different categories, a description of the categorization sequence of the digital images, or a computer-generated electronic label affixed or associated with said digital images.
摘要翻译：通过分析一个或多个文档的数字图像来自动分类一个或多个文档中的一个或多个页面或子文档并自动生成描绘标识符（例如计算机生成的分离图像）的方法和系统，用于描绘文档边界和识别文档类型插入属于不同类别的数字图像之间的页面，数字图像的分类序列的描述，或者附加或与所述数字图像相关联的计算机生成的电子标签。

4. 发明授权

US08374977B2 Methods and systems for transductive data classification 有权
标题翻译：用于转换数据分类的方法和系统
公开(公告)号：US08374977B2
公开(公告)日：2013-02-12
申请号：US12721393
申请日：2010-03-10
申请人： Mauritius A. R. Schmidtler , Christopher K. Harris
发明人： Mauritius A. R. Schmidtler , Christopher K. Harris
IPC分类号： G06N5/00
CPC分类号： G06N99/005
摘要： A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof.
摘要翻译：提供了一种用于对数据进行分类的系统，方法，数据处理装置和制品。标签数据点被接收，每个标记数据点具有至少一个标签，指示数据点是否是用于包括在指定类别中的数据点的训练示例，或者是从指定类别排除的数据点的训练示例; 接收未标记的数据点; 接收标记数据点和未标记数据点的至少一个预定成本因子; 通过使用至少一个成本因子和标记的数据点和未标记的数据点作为训练示例的迭代计算来训练使用MED的转换分类器; 应用经过训练的分类器对未标记的数据点，标记数据点和输入数据点中的至少一个进行分类; 并输出分类数据点或其派生物的分类。

5. 发明授权

US07386527B2 Effective multi-class support vector machine classification 有权
标题翻译：有效的多类支持向量机分类
公开(公告)号：US07386527B2
公开(公告)日：2008-06-10
申请号：US10412163
申请日：2003-04-10
申请人： Christopher K. Harris , Mauritius A. R. Schmidtler
发明人： Christopher K. Harris , Mauritius A. R. Schmidtler
IPC分类号： G06F17/00 , G06F15/18 , G06N7/00 , G06N7/08 , G06N5/00
CPC分类号： G06K9/6269
摘要： An improved method of classifying examples into multiple categories using a binary support vector machine (SVM) algorithm. In one preferred embodiment, the method includes the following steps: storing a plurality of user-defined categories in a memory of a computer; analyzing a plurality of training examples for each category so as to identify one or more features associated with each category; calculating at least one feature vector for each of the examples; transforming each of the at least one feature vectors so as reflect information about all of the training examples; and building a SVM classifier for each one of the plurality of categories, wherein the process of building a SVM classifier further includes: assigning each of the examples in a first category to a first class and all other examples belonging to other categories to a second class, wherein if any one of the examples belongs to another category as well as the first category, such examples are assigned to the first class only; optimizing at least one tunable parameter of a SVM classifier for the first category, wherein the SVM classifier is trained using the first and second classes; and optimizing a function that converts the output of the binary SVM classifier into a probability of category membership.
摘要翻译：一种使用二进制支持向量机（SVM）算法将示例分类为多个类别的改进方法。在一个优选实施例中，该方法包括以下步骤：将多个用户定义的类别存储在计算机的存储器中; 分析每个类别的多个训练示例，以便识别与每个类别相关联的一个或多个特征; 为每个示例计算至少一个特征向量; 转换所述至少一个特征向量中的每一个，以便反映关于所有训练示例的信息; 以及为所述多个类别中的每个类别构建SVM分类器，其中，构建SVM分类器的过程还包括：将第一类别中的每个示例分配给第一类，将属于其他类别的所有其他示例分配给第二类其中如果任何一个示例属于另一类别以及第一类别，则这些示例仅被分配给第一类; 优化用于所述第一类别的SVM分类器的至少一个可调参数，其中使用所述第一类和第二类训练所述SVM分类器; 并优化将二进制SVM分类器的输出转换成类别成员的概率的函数。

6. 发明申请

US20080097936A1 METHODS AND SYSTEMS FOR TRANSDUCTIVE DATA CLASSIFICATION 有权
标题翻译：用于传输数据分类的方法和系统
公开(公告)号：US20080097936A1
公开(公告)日：2008-04-24
申请号：US11752634
申请日：2007-05-23
申请人： Mauritius A. R. Schmidtler , Christopher K. Harris
发明人： Mauritius A. R. Schmidtler , Christopher K. Harris
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Labeled data points are received, each of the labeled data points having at least one label indicating whether the data point is a training example for data points for being included in a designated category or a training example for data points being excluded from a designated category; receiving unlabeled data points; receiving at least one predetermined cost factor of the labeled data points and unlabeled data points; training a transductive classifier using MED through iterative calculation using the at least one cost factor and the labeled data points and the unlabeled data points as training examples; applying the trained classifier to classify at least one of the unlabeled data points, the labeled data points, and input data points; and outputting a classification of the classified data points, or derivative thereof.
摘要翻译：提供了一种用于对数据进行分类的系统，方法，数据处理装置和制品。标签数据点被接收，每个标记数据点具有至少一个标签，指示数据点是否是用于包括在指定类别中的数据点的训练示例，或者是从指定类别排除的数据点的训练示例; 接收未标记的数据点; 接收标记数据点和未标记数据点的至少一个预定成本因子; 通过使用至少一个成本因子和标记的数据点和未标记的数据点作为训练示例的迭代计算来训练使用MED的转换分类器; 应用经过训练的分类器对未标记的数据点，标记数据点和输入数据点中的至少一个进行分类; 并输出分类数据点或其派生物的分类。

7. 发明授权

US09910829B2 Automatic document separation 有权
公开(公告)号：US09910829B2
公开(公告)日：2018-03-06
申请号：US14181497
申请日：2014-02-14
申请人： Mauritius A. R. Schmidtler , Scott S. Texeira , Roland G. Borrey , Anthony Macciola
发明人： Mauritius A. R. Schmidtler , Scott S. Texeira , Christopher K. Harris , Sameer Samat , Roland G. Borrey , Anthony Macciola
IPC分类号： G06F17/21 , G06K9/00 , H04N1/32
CPC分类号： G06F17/21 , G06K9/00442 , H04N1/32112 , H04N2201/3225 , H04N2201/3243
摘要： A method and system for delineating document and/or subdocument boundaries and identifying document and/or subdocument types, the method comprising: automatically generating at least one identifier for identifying which of a plurality of document and/or subdocument images belongs to which of a plurality of categories. The method and/or system optionally may include automatically categorizing a plurality of document and/or subdocument images into a plurality of predetermined categories in accordance with classification rules for said categories.

8. 发明授权

US09160611B2 System and method for performing longest common prefix strings searches 有权
标题翻译：用于执行最长公共前缀字符串搜索的系统和方法
公开(公告)号：US09160611B2
公开(公告)日：2015-10-13
申请号：US12757972
申请日：2010-04-09
申请人： Christopher K. Harris , Hal Lonas
发明人： Christopher K. Harris , Hal Lonas
IPC分类号： G06F7/00 , G06F17/30 , H04L29/12 , H04L12/743 , H04L29/06
CPC分类号： G06F17/30501 , G06F17/30076 , G06F17/30289 , G06F17/30569 , G06F17/30864 , H04L29/12594 , H04L45/7457 , H04L61/30 , H04L61/301 , H04L63/10 , H04L63/168
摘要： A method and system a method for compressing and searching a plurality of strings. The method includes inputting a plurality of strings into a compression engine. The method also includes converting each of the plurality of strings into a new, prefix-preserving compressed string, using the compression engine. For every string P that is a strict prefix of a string S, P's resulting compressed string is a strict prefix of S's resulting compressed string.
摘要翻译：一种方法和系统，用于压缩和搜索多个字符串的方法。该方法包括将多个串输入到压缩引擎中。该方法还包括使用压缩引擎将多个字符串中的每一个转换为新的前缀保留压缩字符串。对于作为字符串S的严格前缀的每个字符串P，P的生成的压缩字符串是S生成的压缩字符串的严格前缀。

9. 发明申请

US20140164914A1 AUTOMATIC DOCUMENT SEPARATION 有权
标题翻译：自动文档分离
公开(公告)号：US20140164914A1
公开(公告)日：2014-06-12
申请号：US14181497
申请日：2014-02-14
申请人： Mauritius A.R. Schmidtler , Scott S. Texeira , Christopher K. Harris , Sameer Samat , Roland G. Borrey , Anthony Macciola
发明人： Mauritius A.R. Schmidtler , Scott S. Texeira , Christopher K. Harris , Sameer Samat , Roland G. Borrey , Anthony Macciola
IPC分类号： G06F17/21
CPC分类号： G06F17/21 , G06K9/00442 , H04N1/32112 , H04N2201/3225 , H04N2201/3243
摘要： A method and system for delineating document and/or subdocument boundaries and identifying document and/or subdocument types, the method comprising: automatically generating at least one identifier for identifying which of a plurality of document and/or subdocument images belongs to which of a plurality of categories. The method and/or system optionally may include automatically categorizing a plurality of document and/or subdocument images into a plurality of predetermined categories in accordance with classification rules for said categories.
摘要翻译：一种用于描绘文档和/或子文档边界并识别文档和/或子文档类型的方法和系统，所述方法包括：自动生成至少一个标识符，用于识别多个文档和/或子文档图像中的哪一个属于多个的类别。方法和/或系统可选地可以包括根据所述类别的分类规则自动将多个文档和/或子文档图像分类成多个预定类别。

10. 发明申请

US20100274786A1 System And Method For Performing Longest Common Prefix Strings Searches 有权
标题翻译：执行最长公共前缀字符串搜索的系统和方法
公开(公告)号：US20100274786A1
公开(公告)日：2010-10-28
申请号：US12757972
申请日：2010-04-09
申请人： Christopher K. Harris , Hal Lonas
发明人： Christopher K. Harris , Hal Lonas
IPC分类号： G06F17/30 , G06F15/16
CPC分类号： G06F17/30501 , G06F17/30076 , G06F17/30289 , G06F17/30569 , G06F17/30864 , H04L29/12594 , H04L45/7457 , H04L61/30 , H04L61/301 , H04L63/10 , H04L63/168
摘要： A method and system a method for compressing and searching a plurality of strings. The method includes inputting a plurality of strings into a compression engine. The method also includes converting each of the plurality of strings into a new, prefix-preserving compressed string, using the compression engine. For every string P that is a strict prefix of a string S, P's resulting compressed string is a strict prefix of S's resulting compressed string.
摘要翻译：一种方法和系统，用于压缩和搜索多个字符串的方法。该方法包括将多个串输入到压缩引擎中。该方法还包括使用压缩引擎将多个字符串中的每一个转换为新的前缀保留压缩字符串。对于作为字符串S的严格前缀的每个字符串P，P的生成的压缩字符串是S生成的压缩字符串的严格前缀。

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