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    • 2. 发明授权
    • Augmenting a training set for document categorization
    • 增加文件分类培训
    • US09058382B2
    • 2015-06-16
    • US12254798
    • 2008-10-20
    • Tie-Yan LiuWei-Ying Ma
    • Tie-Yan LiuWei-Ying Ma
    • G06F7/00G06F17/30
    • G06F17/3071G06N99/005Y10S707/99935
    • A method and system for augmenting a training set used to train a classifier of documents is provided. The augmentation system augments a training set with training data derived from features of documents based on a document hierarchy. The training data of the initial training set may be derived from the root documents of the hierarchies of documents. The augmentation system generates additional training data that includes an aggregate feature that represents the overall characteristics of a hierarchy of documents, rather than just the root document. After the training data is generated, the augmentation system augments the initial training set with the newly generated training data.
    • 提供了一种用于增加用于训练文档分类器的训练集的方法和系统。 增强系统使用基于文档层次结构的文档特征从训练数据中增加训练集。 初始训练集的训练数据可以从文档层级的根文档中导出。 增强系统生成额外的培训数据,其中包括表示文档层次结构的整体特征的聚合特征,而不仅仅是根文档。 在产生训练数据之后,增强系统利用新生成的训练数据增加初始训练集。
    • 7. 发明授权
    • Ranking advertisement(s) based upon advertisement feature(s)
    • 基于广告功能的排名广告
    • US08620912B2
    • 2013-12-31
    • US12816533
    • 2010-06-16
    • Xin-Jing WangLei ZhangWei-Ying Ma
    • Xin-Jing WangLei ZhangWei-Ying Ma
    • G06F7/00G06F17/30
    • G06Q30/0241G06Q30/0251
    • While browsing, a user may interact with a wide variety of images. The user may upload and share images taken with a digital camera and/or search for image using a search engine. Because images are rich in contextual information, it may be advantageous to provide additional information, such as adjacent market advertising based upon matching advertisements with contextual information of the images. Accordingly, a query image may be used to retrieve a video frame set. The video frame set may be expanded with related video frames corresponding to adjacent markets. The expanded video frame set may be grouped into clusters of similar frames. The clusters may be used to rank advertisements based upon how similar the advertisements are to the clusters and/or video frames within the clusters. In this way, one or more ranked advertisements may be presented with the query image.
    • 在浏览时,用户可以与各种图像进行交互。 用户可以上传和共享用数码相机拍摄的图像和/或使用搜索引擎来搜索图像。 由于图像丰富的上下文信息,提供附加信息可能是有利的,例如基于匹配广告的相邻市场广告与图像的上下文信息。 因此,可以使用查询图像来检索视频帧集合。 视频帧集可以用对应于相邻市场的相关视频帧进行扩展。 扩展的视频帧集可以被分组成类似帧的簇。 群集可以用于基于广告与群集内的群集和/或视频帧的相似度来对广告进行排名。 以这种方式,可以向查询图像呈现一个或多个排名的广告。
    • 9. 发明授权
    • Interactive framework for name disambiguation
    • 互动框架的名称消歧
    • US08538898B2
    • 2013-09-17
    • US13118404
    • 2011-05-28
    • Zhengdong LuZaiqing NieGang LuoYong CaoJi-Rong WenWei-Ying Ma
    • Zhengdong LuZaiqing NieGang LuoYong CaoJi-Rong WenWei-Ying Ma
    • G06N5/00
    • G06N99/005G06F17/30616
    • A “Name Disambiguator” provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names.
    • “名称歧义者”提供了各种技术,用于实现用于解析或消除实体名称(与诸如出版物的对象相关联)的交互式框架,用于实体搜索,其中两个或多个相同或相似的名称可以指代不同的实体。 更具体地说,名称消歧器使用用户输入和自动模型的组合来解决消歧问题。 在各种实施例中,名称消歧器使用两部分过程,包括:1)以模拟交互模式从大量文档或对象训练的全局SVM,以及2)本地SVM模型的进一步个性化(与个体名称或组相关联 来自全球SVM模型的名称,例如一组合作者。 这个过程的结果是,大量的文档或对象可以通过按实体名称组织的快速,准确的浓缩或聚类成有序集。