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    • 4. 发明公开
    • GENERATION OF WEIGHTS IN MACHINE LEARNING
    • ERZEUGUNG VON GEWICHTEN在MASCHINENLERNVORGÄNGEN
    • EP3072069A4
    • 2017-08-09
    • EP14863709
    • 2014-11-21
    • CALIFORNIA INST OF TECHN
    • ABU-MOSTAFA YASER SAIDGONZALEZ CARLOS ROBERTO
    • G06N99/00G06K9/62
    • G06N99/005G06K9/6223G06K9/6256G06N5/025G06N7/005
    • Technologies are generally described for systems, devices and methods relating to determining weights in a machine learning environment. In some examples, a training distribution of training data may be identified, information about a test distribution of test data, and a coordinate of the training data and the test data may be identified. Differences between the test distribution and the training distribution may be determined, for the coordinate. A weight importance parameter may be identified, for the coordinate. A processor may calculate weights based on the differences, and based on the weight importance parameter. The weights may be adapted to cause the training distribution to conform to the test distribution at a degree of conformance. The degree of conformance may be based on the weight importance parameter.
    • 技术通常描述与确定机器学习环境中的权重有关的系统,设备和方法。 在一些示例中,可以识别训练数据的训练分布,关于测试数据的测试分布的信息以及训练数据和测试数据的坐标可以被识别。 对于坐标,可以确定测试分布和训练分布之间的差异。 对于坐标,可以识别重量重要性参数。 处理器可以基于差异并基于重量重要性参数来计算权重。 权重可以适合于使得训练分布符合一致性程度的测试分布。 一致性程度可以基于重量重要性参数。
    • 7. 发明公开
    • POSE AND SUB-POSE CLUSTERING-BASED IDENTIFICATION OF INDIVIDUALS
    • IDENTIFIZIERUNG VON PERSONEN AUF基于VON HALTUNGS- UND SUBHALTUNGSGRUPPIERUNG
    • EP3039600A1
    • 2016-07-06
    • EP14767085.5
    • 2014-07-24
    • Tata Consultancy Services Limited
    • SINHA, AniruddhaCHARKRAVARTY, Kingshuk
    • G06F21/31G06K9/00
    • G06F21/31G06K9/00348G06K9/00778G06K9/6219G06K9/6223
    • The subject matter discloses systems and methods for identification of individuals. The method includes obtaining static and dynamic feature vectors for skeleton data frames of each individual performing a step activity with an arbitrary pattern and in a random path; creating, for the each individual, a first predefined number of clusters of dynamic feature vectors for the frames; creating, for the each individual, a second predefined number of sub-clusters within the each of the clusters of the dynamic feature vectors for the frames associated with the each of the clusters; and determining, for the each individual, a gait-pose feature data set based on computation of a center of the dynamic feature vectors for the frames associated with the each of the sub-clusters, and a mean of the static feature vectors for the frames associated with the each of the clusters, for identifying the individuals.
    • 主题公开了用于识别个人的系统和方法。 该方法包括获取静态和动态特征向量,用于以任意模式和随机路径执行步骤活动的每个个体的骨架数据帧; 为每个个体创建用于所述帧的动态特征向量的第一预定数量的群集; 为每个个体创建与每个簇相关联的帧的动态特征向量的每个簇内的第二预定数量的子簇; 以及针对每个个体,基于与每个子簇相关联的帧的动态特征向量的中心的计算来确定步态姿势特征数据集,以及用于帧的静态特征向量的平均值 与每个群集相关联,用于识别个体。
    • 8. 发明公开
    • Clustering database queries for runtime prediction
    • 聚集冯Datenbankabfragen zur Laufzeitvorhersages
    • EP3038018A1
    • 2016-06-29
    • EP14307192.6
    • 2014-12-27
    • Dassault Systèmes
    • Belghiti, Ismael
    • G06K9/62G06F17/30
    • G06F17/30598G06F17/30336G06F17/30463G06F17/30469G06K9/6223
    • The invention notably relates to a computer-implemented method of clustering reference queries in a database for prediction of the runtime of a target query in the database based on similarity of the target query with the reference queries. The method comprises the steps of providing (S10) a number of numerical values that represent the runtimes of the reference queries; computing the optimal K-means clustering of the numerical values for a predetermined number of clusters, wherein the computing step (S20) includes iterating, a number of times corresponding to the predetermined number of clusters, a linear-time Row Minima Searching algorithm applied to a square matrix of order equal to the number of numerical values; and clustering the reference queries according to the computed clustering of the numerical values.
      Such a method improves the field of database query runtime prediction.
    • 本发明特别涉及一种基于计算机实现的在数据库中聚类参考查询的方法,用于基于目标查询与参考查询的相似性来预测数据库中的目标查询的运行时间。 该方法包括以下步骤:提供(S10)表示参考查询的运行时间的数值的数值; 计算预定数量的聚类的数值的最优K均值聚类,其中所述计算步骤(S20)包括迭代与所述预定数量的聚类对应的次数,应用于线性时间行小行星搜索算法 一个等于数值的正方形矩阵; 并根据计算的数值聚类对参考查询进行聚类。 这种方法改进了数据库查询运行时预测的领域。