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    • 3. 发明申请
    • COMPUTING MINIMAL POLYNOMIALS OF RADICAL EXPRESSIONS
    • 计算放射性表达的最小多边形
    • US20100198902A1
    • 2010-08-05
    • US12364533
    • 2009-02-03
    • Xu YangZhouchen LinSijun LiuTianjun Ye
    • Xu YangZhouchen LinSijun LiuTianjun Ye
    • G06F7/552
    • G06F17/10
    • Described is a technology, such as implemented in a computational software program, by which a minimal polynomial is efficiently determined for a radical expression based upon its structure of the radical expression. An annihilation polynomial is found based upon levels of the radical to obtain roots of the radical. A numerical method performs a zero test or multiple zero tests to find the minimal polynomial. In one implementation, the set of roots corresponding to a radical expression is found. The annihilation polynomial is computed by grouping roots of the set according to their conjugation relationship and multiplying factor polynomials level by level. A selection mechanism selects the minimal polynomial based upon the annihilation polynomial's factors.
    • 描述了一种技术,例如在计算软件程序中实现的技术,通过该技术,基于其基本表达式的结构,有效地确定基本表达式的最小多项式。 基于获得根的根的自由基的水平找到湮灭多项式。 数值方法执行零测试或多零测试以找到最小多项式。 在一个实现中,找到与激进表达相对应的一组根。 湮灭多项式通过根据它们的共轭关系和乘法因子多项式级别逐级分组的根来计算。 选择机制根据湮灭多项式的因素选择最小多项式。
    • 10. 发明申请
    • CLASSIFICATION VIA SEMI-RIEMANNIAN SPACES
    • 通过SEMI-RIEMANNIAN SPACES分类
    • US20100080450A1
    • 2010-04-01
    • US12242421
    • 2008-09-30
    • Deli ZhaoZhouchen LinXiaoou Tang
    • Deli ZhaoZhouchen LinXiaoou Tang
    • G06K9/62
    • G06K9/6234G06K9/6252
    • Described is using semi-Riemannian geometry in supervised learning to learn a discriminant subspace for classification, e.g., labeled samples are used to learn the geometry of a semi-Riemannian submanifold. For a given sample, the K nearest classes of that sample are determined, along with the nearest samples that are in other classes, and the nearest samples in that sample's same class. The distances between these samples are computed, and used in computing a metric matrix. The metric matrix is used to compute a projection matrix that corresponds to the discriminant subspace. In online classification, as a new sample is received, it is projected into a feature space by use of the projection matrix and classified accordingly.
    • 描述了在监督学习中使用半黎曼几何学习学习用于分类的判别子空间,例如,标记的样本用于学习半黎曼子流形歧管的几何形状。 对于给定的样本,该样本的K个最近类别以及其他类别中最近的样本以及该样本同一类中最近的样本进行确定。 计算这些样本之间的距离,并用于计算度量矩阵。 度量矩阵用于计算与判别子空间对应的投影矩阵。 在线分类中,作为收到的新样本,通过使用投影矩阵将其投影到特征空间中并进行分类。