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    • 4. 发明申请
    • Method and apparatus for classifying data using R-functions
    • 使用R功能对数据进行分类的方法和装置
    • US20070244843A1
    • 2007-10-18
    • US11387253
    • 2006-03-22
    • Anton BougaevAleksey Urmanov
    • Anton BougaevAleksey Urmanov
    • G06N5/02
    • G06N7/005G06K9/6287
    • One embodiment of the present invention provides a system that constructs a classifier that distinguishes between different classes of data points. During operation, the system first receives a data set, which includes class-one data points and class-two data points. For each class-one data point in the data set, the system uses a separating primitive to produce a set of point-to-point separating boundaries, wherein each point-to-point separating boundary separates the class-one data point from a different class-two data point. Next, the system combines separating boundaries in the set of separating boundaries to produce a point-to-class separating boundary that separates the class-one data point from all of the class-two data points in the data set. Finally, the system combines the point-to-class separating boundaries for each of the class-one data points to produce a class-to-class separating boundary for the classifier that separates all of the class-one data points from all of the class-two data points in the data set.
    • 本发明的一个实施例提供一种构建区分不同类别的数据点的分类器的系统。 在操作过程中,系统首先接收一个包含一类数据点和二类数据点的数据集。 对于数据集中的每个一类数据点,系统使用分离原语来产生一组点对点分离边界,其中每个点到点分离边界将一类数据点与不同的 二级数据点。 接下来,该系统组合分离边界集合中的分离边界以产生将一类数据点与数据集中的所有二类数据点分离的点对数分离边界。 最后,该系统组合了一类数据点中的每一个数据点的点对点分离边界,从而为分类器生成一个类到类的分离边界,将所有类的一个数据点与所有类分开 - 数据集中的两个数据点。
    • 5. 发明申请
    • DECISION MAKING WITH ANALYTICALLY COMBINED SPLIT CONDITIONS
    • 决策采用分析性组合分割条件
    • US20130103618A1
    • 2013-04-25
    • US13279447
    • 2011-10-24
    • Aleksey URMANOVAnton Bougaev
    • Aleksey URMANOVAnton Bougaev
    • G06F15/18
    • G06N99/005G06K9/6282
    • Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples.
    • 提供了与分析组合拆分条件的决策相关联的系统,方法和其他实施例。 在一个实施例中,提供了一种用于对数据进行分类的方法。 接收输入数据样本作为属于两个可能类别之一的分类。 输入数据样本包括一组属性值。 该方法包括用定义分类树的决策边界的树函数来评估属性值集合。 至少部分地基于输入数据样本的属性值,树函数将输入数据样本归类为属于两个可能类之一的输入数据样本。 在另一个实施例中,通过对训练数据样本应用梯度下降参数更新规则来导出树函数的参数。
    • 6. 发明授权
    • Decision making with analytically combined split conditions
    • 通过分析组合拆分条件进行决策
    • US08868473B2
    • 2014-10-21
    • US13279447
    • 2011-10-24
    • Aleksey UrmanovAnton Bougaev
    • Aleksey UrmanovAnton Bougaev
    • G06K9/62G06N99/00
    • G06N99/005G06K9/6282
    • Systems, methods, and other embodiments associated with decision making with analytically combined split conditions are provided. In one embodiment, a method for classifying data is provided. An input data sample is received for classification as belonging to one of two possible classes. The input data sample includes a set of attribute values. The method includes evaluating the set of attribute values with a tree function that defines a decision boundary of a classification tree. The tree function classifies an input data sample as belonging to one of the two possible classes based, at least in part, on the attribute values of the input data sample. In another embodiment parameters of the tree function are derived by applying a gradient descent parameter update rule to the training data samples.
    • 提供了与分析组合拆分条件的决策相关联的系统,方法和其他实施例。 在一个实施例中,提供了一种用于对数据进行分类的方法。 接收输入数据样本作为属于两个可能类别之一的分类。 输入数据样本包括一组属性值。 该方法包括用定义分类树的决策边界的树函数来评估属性值集合。 至少部分地基于输入数据样本的属性值,树函数将输入数据样本归类为属于两个可能类之一的输入数据样本。 在另一个实施例中,通过对训练数据样本应用梯度下降参数更新规则来导出树函数的参数。
    • 7. 发明授权
    • Method and apparatus for canceling fan noise in a computer system
    • 在计算机系统中消除风扇噪声的方法和装置
    • US07693292B1
    • 2010-04-06
    • US11205473
    • 2005-08-16
    • Kenny C. GrossAleksey UrmanovAnton Bougaev
    • Kenny C. GrossAleksey UrmanovAnton Bougaev
    • H03B29/00
    • G10K11/178
    • One embodiment of the present invention provides a system that cancels fan noise in a computer system. During operation, the system obtains a fan noise signal using a microphone. Next, the system generates a spectral pattern based on the obtained fan noise signal. The system then uses the spectral pattern to identify a corresponding cancellation spectrum in an anti-spectra library. Next, the system generates a noise-canceling signal using the cancellation spectrum. Note that the amount of computation required to cancel fan noise is reduced because generating the noise-canceling signal using the anti-spectra library requires less computation than generating the noise-canceling signal using dynamic noise-cancellation techniques.
    • 本发明的一个实施例提供了一种抵消计算机系统中的风扇噪声的系统。 在操作过程中,系统使用麦克风获取风扇噪声信号。 接下来,系统基于获得的风扇噪声信号产生频谱图案。 然后,系统使用光谱图案来识别反谱库中相应的消除光谱。 接下来,系统使用消除频谱产生噪声消除信号。 注意,消除风扇噪声所需的计算量减少,因为使用反频谱库产生噪声消除信号比使用动态噪声消除技术产生噪声消除信号需要更少的计算。