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    • 13. 发明授权
    • Method and apparatus for progressively selecting features from a large feature space in statistical modeling
    • 在统计建模中从大特征空间逐步选择特征的方法和装置
    • US08019594B2
    • 2011-09-13
    • US11478990
    • 2006-06-30
    • Fuliang WengZhe FengQi Zhang
    • Fuliang WengZhe FengQi Zhang
    • G06F17/27
    • G06F17/277G06K9/6228G10L15/02G10L15/063
    • Embodiments of a progressive feature selection method that selects features in multiple rounds are described. In one embodiment, the progressive feature selection method splits the feature space into tractable sub-spaces such that a feature selection algorithm can be performed on each sub-space. In a merge-split operation, the subset of features that the feature selection algorithm selects from the different sub-spaces are merged into subsequent sets of features. Instead of re-generating the mapping table for each subsequent set from scratch, a new mapping table from the previous round's tables is created by collecting those entries that correspond to the selected features. The feature selection method is then performed again on each of the subsequent feature sets and new features are selected from each of these feature sets. This feature selection-merge-split process is repeated on successively smaller numbers of feature sets until a single final set of features is selected.
    • 描述了选择多个轮中的特征的渐进特征选择方法的实施例。 在一个实施例中,逐行特征选择方法将特征空间分解成易处理的子空间,使得可以对每个子空间执行特征选择算法。 在合并分割操作中,特征选择算法从不同子空间中选择的特征子集被合并到随后的特征集中。 不是从头重新生成每个后续集合的映射表,而是通过收集与所选特征相对应的条目来创建来自前一轮的表的新映射表。 然后在每个后续特征集上再次执行特征选择方法,并且从这些特征集中的每一个中选择新特征。 在连续较小数量的特征集上重复该特征选择合并分割处理,直到选择单个最终特征集。
    • 14. 发明授权
    • Method and apparatus for generating features through logical and functional operations
    • 用于通过逻辑和功能操作产生特征的方法和装置
    • US08019593B2
    • 2011-09-13
    • US11478989
    • 2006-06-30
    • Fuliang WengZhe FengQi Zhang
    • Fuliang WengZhe FengQi Zhang
    • G06F17/27
    • G10L15/18G06K9/6228G06K9/6232G10L15/02
    • Embodiments of a feature generation system and process for use in machine learning applications utilizing statistical modeling systems are described. In one embodiment, the feature generation process generates large feature spaces by combining features using logical, arithmetic and/or functional operations. A first set of features in an initial feature space are defined. Some or all of the first set of features are processed using one or more arithmetic, logic, user-defined combinatorial processes, or combinations thereof, to produce additional features. The additional features and at least some of the first set of features are combined to produce an expanded feature space. The expanded feature space is processed through a feature selection and optimization process to produce a model in a statistical modeling system.
    • 描述了使用统计建模系统的特征生成系统和用于机器学习应用的过程的实施例。 在一个实施例中,特征生成过程通过使用逻辑,算术和/或功能操作组合特征来生成大的特征空间。 定义初始特征空间中的第一组特征。 使用一个或多个算术,逻辑,用户定义的组合过程或其组合来处理第一组特征中的一些或全部,以产生附加特征。 附加特征和第一组特征中的至少一些被组合以产生扩展的特征空间。 扩展的特征空间通过特征选择和优化过程进行处理,以在统计建模系统中产生模型。
    • 20. 发明授权
    • Method and apparatus for recognizing large list of proper names in spoken dialog systems
    • 在口头对话系统中识别大名单的方法和装置
    • US07925507B2
    • 2011-04-12
    • US11483840
    • 2006-07-07
    • Fuliang WengTobias ScheideckZhe FengBadri Raghunathan
    • Fuliang WengTobias ScheideckZhe FengBadri Raghunathan
    • G10L15/18
    • G06F17/278G06F17/30654G10L15/22
    • Embodiments of a name recognition process for use in dialog systems are described. In one embodiment, the name recognition process assigns weighting values to names used in a dialog based on the usage of these names. This process takes advantage of the general tendency of people to speak names, either full or partial, only after they have heard or read these names. Name input is taken in several different forms, including a static background database that contains all possible names, a background database that contains commonly used names (such as common trademarks or references), a database that contains names from a user model, and a dynamic database that constantly takes the names just mentioned. The names are then appended with proper weighting values. A high weight is given to names that have been mentioned recently, a lower weight is given to common names, and a lowest weight is given to names for the ones that have never been used or mentioned.
    • 描述在对话系统中使用的名称识别过程的实施例。 在一个实施例中,名称识别过程基于这些名称的使用,将加权值分配给对话中使用的名称。 这个过程只有在听到或读过这些名字之后才能利用人们的全部或部分名字的一般倾向。 名称输入采用几种不同的形式,包括包含所有可能名称的静态后台数据库,包含常用名称(如公共商标或引用)的后台数据库,包含用户模型名称的数据库和动态 数据库不断提供刚才提到的名称。 然后,这些名称将附加适当的权重值。 给予最近提到的名称较高的重量,较低的重量被赋予通用名称,而对于从未被使用或提及的那些的名称来说,最小的重量是给予的。