公开(公告)号：US20080133503A1

公开(公告)日：2008-06-05

申请号：US11607299

申请日：2006-11-30

申请人： Alexandrin Popescul ,  Clifford A. Brunk ,  Byron Dom

发明人： Alexandrin Popescul ,  Clifford A. Brunk ,  Byron Dom

IPC分类号： G06F7/10 ,  G06F17/30

CPC分类号： G06F17/30864 ,  G06Q30/02 ,  G06Q30/0275 ,  G06Q30/0609 ,  G06Q30/08

摘要： A system and method are disclosed for identifying a keyword that is a novel concept or anomaly based on prior search results for the keyword. Advertisements may be sold for the keyword, or the keyword may be purchased or recommended for purchase based on anticipation of increased future searches on the keyword.

摘要翻译： 公开了一种用于基于关键字的先前搜索结果来识别作为新概念或异常的关键字的系统和方法。 可以为关键字出售广告，或者可以基于对关键字的未来搜索量的预期，购买或推荐关键字进行购买。

公开(公告)号：US20070214140A1

公开(公告)日：2007-09-13

申请号：US11373726

申请日：2006-03-10

申请人： Byron Dom ,  Hui Han ,  Ramnath Balasubramanyan ,  Dmitry Pavlov

发明人： Byron Dom ,  Hui Han ,  Ramnath Balasubramanyan ,  Dmitry Pavlov

IPC分类号： G06F7/00

CPC分类号： G06F17/30707

摘要： Techniques are described for assigning, to target categories of a target scheme, items that have been obtained from a plurality of sources. In situations in which one or more of the sources has organized its information according to a source scheme that differs from the target scheme, the assignment may be based, in part, on an estimate of the probability that items from a particular source category should be assigned to a particular target category. Such probability estimates may be based on how many training set items associated with the particular source category have been assigned to the particular target category. Source categories may be grouped into clusters. The probability estimates may also be based on how many training set items within the cluster to which the particular source category has been mapped, have been assigned the particular target category.

摘要翻译： 描述了将目标方案的目标类别分配给从多个源获得的项目的技术。 在一个或多个来源已经根据与目标方案不同的源方案来组织其信息的情况下，分配可以部分地基于来自特定源类别的项目应该是 分配到特定目标类别。 这种概率估计可以基于与特定源类别相关联的多少训练集项目已被分配给特定目标类别。 源类别可以分组成簇。 概率估计还可以基于特定源类别已映射到的集群内的多少个训练集项目已被分配给特定的目标类别。

公开(公告)号：US08611675B2

公开(公告)日：2013-12-17

申请号：US11644172

申请日：2006-12-22

申请人： Jyh-Herng Chow ,  Byron Dom ,  Dao-I Lin

发明人： Jyh-Herng Chow ,  Byron Dom ,  Dao-I Lin

IPC分类号： G06K9/62

CPC分类号： G06K9/6253

摘要： Techniques are described herein for generating and displaying a confusion matrix wherein a data item belonging to one or more actual classes is predicted into a class. The classes in which the data item may be predicted (the “predicted classes”) are ranked according to a score that in one embodiment indicates the confidence of the prediction. If the data item is predicted into a class that is one of the top K ranked predicted classes, then the prediction is considered accurate and an entry is created in a cell of a confusion matrix indicating the accurate prediction. If the data item is not predicted into a class that is not one of the top K ranked predicted classes, then the prediction is considered inaccurate and an entry is created in a cell of a confusion matrix indicating the inaccurate prediction.

摘要翻译： 本文描述了用于生成和显示混淆矩阵的技术，其中属于一个或多个实际类的数据项被预测为类。 可以根据在一个实施例中指示预测的置信度的分数对可以预测数据项目的类别（“预测类别”）进行排名。 如果数据项目被预测为属于顶级K级预测类别之一的类别，那么该预测被认为是准确的，并且在混合矩阵的小区中创建表示准确预测的条目。 如果数据项目未被预测为不属于顶级K级预测类别的类别，那么该预测被认为是不准确的，并且在混淆矩阵的小区中创建一个表示不准确预测的条目。