利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

78 条记录 (耗时 0.056 秒)

    Resource-light method and apparatus for outlier detection
    3.
    发明授权
    Resource-light method and apparatus for outlier detection 失效
    资源光法和异常检测装置

    公开(公告)号：US08006157B2

    公开(公告)日：2011-08-23

    申请号：US11863704

    申请日：2007-09-28

    申请人： Naoki Abe ,  John Langford

    发明人： Naoki Abe ,  John Langford

    IPC分类号： H04L1/00 ,  G06F11/30 ,  H03M13/00

    CPC分类号： G06N99/005 ,  Y10S707/99936 ,  Y10S707/99943

    摘要： Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.

    摘要翻译： 异常值检测方法和装置具有较轻的计算资源需求，特别是对存储要求的要求，而且具有最先进的预测性能。 异常值检测问题首先降低到分类学习问题，然后应用基于预测不确定度的选择性抽样来进一步减少数据分析所需的数据量，从而提高预测性能。 归类分类主要在于使用未标记的正常数据作为正例，随机生成合成实例作为阴性实例。 选择性抽样的应用使用了基础的，任意的分类学习算法，由上述过程标记的数据，并且迭代地进行。 每个迭代包括从输入数据中选择较小的子样本，对所选数据训练底层分类算法，以及通过分类算法存储分类器输出。 选择是通过基本上选择难以对上述迭代中获得的分类器进行分类的示例来完成的。 最终输出假设是在上述过程的迭代中获得的分类器的投票函数。

    Resource-light method and apparatus for outlier detection
    4.
    发明授权
    Resource-light method and apparatus for outlier detection 有权
    资源光法和异常检测装置

    公开(公告)号：US07296018B2

    公开(公告)日：2007-11-13

    申请号：US10749518

    申请日：2004-01-02

    申请人： Naoki Abe ,  John Langford

    发明人： Naoki Abe ,  John Langford

    IPC分类号： G06F17/00

    CPC分类号： G06N99/005 ,  Y10S707/99936 ,  Y10S707/99943

    摘要： Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.

    摘要翻译： 异常值检测方法和装置具有较轻的计算资源需求，特别是对存储要求的要求，而且具有最先进的预测性能。 异常值检测问题首先降低到分类学习问题，然后应用基于预测不确定度的选择性抽样来进一步减少数据分析所需的数据量，从而提高预测性能。 归类分类主要在于使用未标记的正常数据作为正例，随机生成合成实例作为阴性实例。 选择性抽样的应用使用了基础的，任意的分类学习算法，由上述过程标记的数据，并且迭代地进行。 每个迭代包括从输入数据中选择较小的子样本，对所选数据训练底层分类算法，以及通过分类算法存储分类器输出。 选择是通过基本上选择难以对上述迭代中获得的分类器进行分类的示例来完成的。 最终输出假设是在上述过程的迭代中获得的分类器的投票函数。

    Resource-light method and apparatus for outlier detection
    5.
    发明申请
    Resource-light method and apparatus for outlier detection 有权
    资源光法和异常检测装置

    公开(公告)号：US20050160340A1

    公开(公告)日：2005-07-21

    申请号：US10749518

    申请日：2004-01-02

    申请人： Naoki Abe ,  John Langford

    发明人： Naoki Abe ,  John Langford

    IPC分类号： G06F11/00 ,  G06F11/30 ,  G08C25/00 ,  H03M13/00 ,  H04L1/00

    CPC分类号： G06N99/005 ,  Y10S707/99936 ,  Y10S707/99943

    摘要： Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.

    摘要翻译： 异常值检测方法和装置具有较轻的计算资源需求，特别是对存储要求的要求，而且具有最先进的预测性能。 异常值检测问题首先降低到分类学习问题，然后应用基于预测不确定度的选择性抽样来进一步减少数据分析所需的数据量，从而提高预测性能。 归类分类主要在于使用未标记的正常数据作为正例，随机生成合成实例作为阴性实例。 选择性抽样的应用使用了基础的，任意的分类学习算法，由上述过程标记的数据，并且迭代地进行。 每个迭代包括从输入数据中选择较小的子样本，对所选数据训练底层分类算法，以及通过分类算法存储分类器输出。 选择是通过基本上选择难以对上述迭代中获得的分类器进行分类的示例来完成的。 最终输出假设是在上述过程的迭代中获得的分类器的投票函数。

    Resource-Light Method and apparatus for Outlier Detection
    6.
    发明申请
    Resource-Light Method and apparatus for Outlier Detection 失效
    资源光方法和异常检测装置

    公开(公告)号：US20080022177A1

    公开(公告)日：2008-01-24

    申请号：US11863704

    申请日：2007-09-28

    申请人： Naoki Abe ,  John Langford

    发明人： Naoki Abe ,  John Langford

    IPC分类号： G06F11/30

    CPC分类号： G06N99/005 ,  Y10S707/99936 ,  Y10S707/99943

    摘要： Outlier detection methods and apparatus have light computational resources requirement, especially on the storage requirement, and yet achieve a state-of-the-art predictive performance. The outlier detection problem is first reduced to that of a classification learning problem, and then selective sampling based on uncertainty of prediction is applied to further reduce the amount of data required for data analysis, resulting in enhanced predictive performance. The reduction to classification essentially consists in using the unlabeled normal data as positive examples, and randomly generated synthesized examples as negative examples. Application of selective sampling makes use of an underlying, arbitrary classification learning algorithm, the data labeled by the above procedure, and proceeds iteratively. Each iteration consisting of selection of a smaller sub-sample from the input data, training of the underlying classification algorithm with the selected data, and storing the classifier output by the classification algorithm. The selection is done by essentially choosing examples that are harder to classify with the classifiers obtained in the preceding iterations. The final output hypothesis is a voting function of the classifiers obtained in the iterations of the above procedure.

    摘要翻译： 异常值检测方法和装置具有较轻的计算资源需求，特别是对存储要求的要求，而且具有最先进的预测性能。 异常值检测问题首先降低到分类学习问题，然后应用基于预测不确定度的选择性抽样来进一步减少数据分析所需的数据量，从而提高预测性能。 归类分类主要在于使用未标记的正常数据作为正例，随机生成合成实例作为阴性实例。 选择性抽样的应用使用了基础的，任意的分类学习算法，由上述过程标记的数据，并且迭代地进行。 每个迭代包括从输入数据中选择较小的子样本，对所选数据训练底层分类算法，以及通过分类算法存储分类器输出。 选择是通过基本上选择难以对上述迭代中获得的分类器进行分类的示例来完成的。 最终输出假设是在上述过程的迭代中获得的分类器的投票函数。