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Systems and methods of discovering mixtures of models within data and probabilistic classification of data according to the model mixture

机译:发现数据中的模型混合以及根据模型混合对数据进行概率分类的系统和方法

摘要

Discovering mixtures of models includes: initiating learning algorithms, determining, data sets including a cluster of points in a first region of a domain and a set of points distributed near a first line extending across the domain; inferencing parameters from the cluster and the set of points; creating a description of the cluster of points in the first region of the domain and computing approximations of a first learned mixture model and a second learned mixture model; determining a first and second probability, generating a confidence rating that each point of the cluster of points in the first region of the domain corresponds to the first learned mixture model and generating a confidence rating that each point of the set of points distributed near the first line correspond to the second learned mixture model, thus causing determinations of behavior of a system described by the learned mixture models.
机译:发现模型的混合包括:启动学习算法,确定数据集,该数据集包括域的第一区域中的一组点和分布在跨该域延伸的第一条线附近的一组点;从聚类和点集推论参数;在该域的第一区域中创建点簇的描述,并计算第一学习的混合模型和第二学习的混合模型的近似值;确定第一和第二概率,生成域的第一区域中的点簇的每个点对应于第一学习混合模型的置信度,并生成点的集合中的每个点在第一点附近分布的置信度线对应于第二学习混合物模型,因此导致确定由学习混合物模型描述的系统的行为。

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