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1. 发明专利

AU2003291738A8 Effective multi-class support vector machine classification 未知
公开(公告)号：AU2003291738A8
公开(公告)日：2004-06-30
申请号：AU2003291738
申请日：2003-11-04
申请人： KOFAX IMAGE PRODUCTS INC
发明人： HARRIS CHRISTOPHER K , SCHMIDTLER MAURITIUS A R
IPC分类号： G06F20060101 , G06F1/00 , G06F7/00 , G06F17/00 , G06K9/62
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.

2. 发明申请

WO2008008142A3 MACHINE LEARNING TECHNIQUES AND TRANSDUCTIVE DATA CLASSIFICATION 审中-公开
标题翻译：机器学习技术和传输数据分类
公开(公告)号：WO2008008142A3
公开(公告)日：2008-12-04
申请号：PCT/US2007013484
申请日：2007-06-07
申请人： KOFAX IMAGE PRODUCTS INC , SCHMIDTLER MAURITIUS A R , HARRIS CHRISTOPHER K , BORREY ROLAND , SARAH ANTHONY , CARUSO NICOLA
发明人： SCHMIDTLER MAURITIUS A R , HARRIS CHRISTOPHER K , BORREY ROLAND , SARAH ANTHONY , CARUSO NICOLA
IPC分类号： G06F15/18 , G06F17/00 , G06N5/00 , G06N5/02
CPC分类号： G06K9/6217 , G06N99/005 , Y02A90/22 , Y02A90/26
摘要： A system, method, data processing apparatus, and article of manufacture are provided for classifying data. Data classification methods using machine learning techniques are also disclosed.
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。还公开了使用机器学习技术的数据分类方法。

3. 发明申请

WO2004053630A3 EFFECTIVE MULTI-CLASS SUPPORT VECTOR MACHINE CLASSIFICATION 审中-公开
标题翻译：有效的多级支持向量机分类
公开(公告)号：WO2004053630A3
公开(公告)日：2005-09-29
申请号：PCT/US0335117
申请日：2003-11-04
申请人： KOFAX IMAGE PRODUCTS INC
发明人： HARRIS CHRISTOPHER K , SCHMIDTLER MAURITIUS A R
IPC分类号： G06F20060101 , G06F1/00 , G06F7/00 , G06F17/00 , G06K9/62
CPC分类号： G06K9/6269
摘要： An improved method of classifying examples into multiple categories using a binary 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 to 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：使用二进制向量机（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; 变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。

4. 发明公开

EP1576440A4 EFFECTIVE MULTI-CLASS SUPPORT VECTOR MACHINE CLASSIFICATION 审中-公开
标题翻译：效果激动人心的 - 中等强度的中等强度分娩
公开(公告)号：EP1576440A4
公开(公告)日：2007-04-25
申请号：EP03768631
申请日：2003-11-04
申请人： KOFAX IMAGE PRODUCTS INC
发明人： HARRIS CHRISTOPHER K , SCHMIDTLER MAURITIUS A R
IPC分类号： G06F20060101 , G06F1/00 , G06F7/00 , G06F17/00 , G06K9/62 , G06K9/00
CPC分类号： G06K9/6269
摘要： An improved method of classifying examples into multiple categories using a binary 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 to 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：使用二进制向量机（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; 变换所述至少一个特征向量中的每一个以反映关于所有训练示例的信息; 以及针对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例子分配给第一类别，并将属于其他类别的所有其他例子分配给第二类别其中如果任何一个示例属于另一个类别以及第一类别，则这样的示例仅被分配给第一类别; 优化第一类别的SVM分类器的至少一个可调参数，其中使用第一和第二类训练SVM分类器; 并优化将二进制SVM分类器的输出转换为分类成员的概率的函数。

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