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    Enhancing knowledge discovery from multiple data sets using multiple support vector machines
    11.
    发明授权
    Enhancing knowledge discovery from multiple data sets using multiple support vector machines 有权
    使用多个支持向量机增强来自多个数据集的知识发现

    公开(公告)号：US06658395B1

    公开(公告)日：2003-12-02

    申请号：US09578011

    申请日：2000-05-24

    申请人： Stephen D. Barnhill

    发明人： Stephen D. Barnhill

    IPC分类号： G06F1518

    CPC分类号： G06K9/6269 ,  G06K9/6256 ,  G06N99/005

    摘要： A system and method for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Pre-processing data may involve transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the data that may be derived. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine.

    摘要翻译： 一种用于通过使用多个学习机器的数据来增强知识发现的系统和方法，特别是多个支持向量机。 学习机器的训练数据被预先处理，以便增加其意义。 预处理数据可能涉及变换数据点和/或扩展数据点。 通过增加数据的含义，学习机器被提供更多的处理信息。 特别是对于支持向量机，处理的信息量越大，可能推导出的数据越好。 每个包含不同内核的多个支持向量机用预处理的训练数据进行训练，并用以相同方式预处理的测试数据进行测试。 比较来自多个支持向量机的测试输出，以确定哪个测试输出（如果有的话）代表最优解。 可以调整一个或多个内核的选择，并且可以对一个或多个支持向量机进行再培训和再测试。 可以组合基于不同输入数据集的最佳解决方案以形成要输入到一个或多个附加支持向量机的新输入数据集。

    Enhancing knowledge discovery using multiple support vector machines
    12.
    发明授权
    Enhancing knowledge discovery using multiple support vector machines 有权
    使用多个支持向量机增强知识发现

    公开(公告)号：US6128608A

    公开(公告)日：2000-10-03

    申请号：US303387

    申请日：1999-05-01

    申请人： Stephen D. Barnhill

    发明人： Stephen D. Barnhill

    IPC分类号： G06F20060101 ,  G06F15/18 ,  G06K9/62 ,  G06E1/00

    CPC分类号： G06K9/6256 ,  G06K9/6269 ,  G06N99/005

    摘要： A system and method for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Pre-processing data may involve transforming the data points and/or expanding the data points. By adding meaning to the data, the learning machine is provided with a greater amount of information for processing. With regard to support vector machines in particular, the greater the amount of information that is processed, the better generalizations about the data that may be derived. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. When it is determined that an optimal solution has been achieved, live data is pre-processed and input into the support vector machine comprising the kernel that produced the optimal solution. The live output from the learning machine may then be post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.

    摘要翻译： 一种用于通过使用多个学习机器的数据来增强知识发现的系统和方法，特别是多个支持向量机。 学习机器的训练数据被预先处理，以便增加其意义。 预处理数据可能涉及变换数据点和/或扩展数据点。 通过增加数据的含义，学习机器被提供更多的处理信息。 特别是对于支持向量机，处理的信息量越大，可能推导出的数据越好。 每个包含不同内核的多个支持向量机用预处理的训练数据进行训练，并用以相同方式预处理的测试数据进行测试。 比较来自多个支持向量机的测试输出，以确定哪个测试输出（如果有的话）代表最优解。 可以调整一个或多个内核的选择，并且可以对一个或多个支持向量机进行再培训和再测试。 当确定已经达到最佳解时，实时数据被预处理并输入到产生最佳解的内核的支持向量机中。 然后，来自学习机器的实时输出可以被后处理成由计算机导出的字母数字分类器，用于人或计算机自动化过程的解释。