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    • 1. 发明申请
    • SYSTEM AND METHOD FOR CROSS DOMAIN LEARNING FOR DATA AUGMENTATION
    • 用于数据接收的跨域学习的系统和方法
    • US20110071965A1
    • 2011-03-24
    • US12566270
    • 2009-09-24
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuAnne Ya Zhang
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuAnne Ya Zhang
    • G06F15/18G06N5/02
    • G06N99/005H04L51/12
    • According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.
    • 根据示例性实施例,一种方法包括执行专用计算装置的指令,对于具有多个原始标签的标记源域数据,使用目标函数为标记的源域数据生成多个第一预测标签, 通过使用来自标记的目标域数据的多个标签确定目标函数。 该方法还包括由专用计算装置执行指令以对源域数据的第一预测标签和源域数据的原始标签应用标签关系函数,以确定用于标记的源域数据的多个权重因子 。 该方法还包括执行专用计算装置的指令,以使用标记的目标域数据,标记的源域数据和标记的源域数据的加权因子来生成新的目标函数,并评估新目标的性能 确定是否存在收敛的功能。
    • 2. 发明授权
    • System and method for cross domain learning for data augmentation
    • 用于数据增强的跨域学习的系统和方法
    • US08332334B2
    • 2012-12-11
    • US12566270
    • 2009-09-24
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuYa Zhang
    • Bo LongBelle TsengSudarshan LamkhedeSrinivas VadrevuYa Zhang
    • G06F15/18
    • G06N99/005H04L51/12
    • According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.
    • 根据示例性实施例,一种方法包括执行专用计算装置的指令,对于具有多个原始标签的标记源域数据,使用目标函数为标记的源域数据生成多个第一预测标签, 通过使用来自标记的目标域数据的多个标签确定目标函数。 该方法还包括由专用计算装置执行指令以对源域数据的第一预测标签和源域数据的原始标签应用标签关系函数,以确定用于标记的源域数据的多个权重因子 。 该方法还包括执行专用计算装置的指令,以使用标记的目标域数据,标记的源域数据和标记的源域数据的加权因子来生成新的目标函数,并评估新目标的性能 确定是否存在收敛的功能。
    • 6. 发明申请
    • CLUSTERING OF SEARCH RESULTS
    • 搜索结果的聚集
    • US20120016877A1
    • 2012-01-19
    • US12835954
    • 2010-07-14
    • Srinivas VadrevuYi ChangZhaohui ZhengBo Long
    • Srinivas VadrevuYi ChangZhaohui ZhengBo Long
    • G06F17/30
    • G06F17/30705G06F17/30011G06F17/30675G06F17/30696
    • One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.
    • 一个特定实施例使用一个或多个聚类算法来聚集多个文档以获得一个或多个第一组聚类,其中:每个第一组聚类是使用聚类算法之一聚类文档而得到的; 并且对于每个第一组集合,每个文档属于来自第一组集合的集群之一; 访问搜索查询; 识别响应于搜索查询的搜索结果,其中所述搜索结果包括所述文档中的两个或更多个; 并且聚集搜索结果以获得第二组聚类,其中搜索结果的每个文档属于来自第二组聚类的聚类中的一个。
    • 7. 发明授权
    • Clustering of search results
    • 搜索结果的聚类
    • US09443008B2
    • 2016-09-13
    • US12835954
    • 2010-07-14
    • Srinivas VadrevuYi ChangZhaohui ZhengBo Long
    • Srinivas VadrevuYi ChangZhaohui ZhengBo Long
    • G06F17/30
    • G06F17/30705G06F17/30011G06F17/30675G06F17/30696
    • One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.
    • 一个特定实施例使用一个或多个聚类算法来聚集多个文档以获得一个或多个第一组聚类,其中:每个第一组聚类是使用聚类算法之一聚类文档而得到的; 并且对于每个第一组集合,每个文档属于来自第一组集合的集群之一; 访问搜索查询; 识别响应于搜索查询的搜索结果,其中搜索结果包括两个或更多个文档; 并且聚集搜索结果以获得第二组聚类,其中搜索结果的每个文档属于来自第二组聚类的聚类中的一个。
    • 9. 发明申请
    • UTILIZING OFFLINE CLUSTERS FOR REALTIME CLUSTERING OF SEARCH RESULTS
    • 利用搜索结果实时聚类的离线群集
    • US20120284275A1
    • 2012-11-08
    • US13099197
    • 2011-05-02
    • Srinivas VadrevuChoon Hui TeoSuju RajanKunal PuneraByron E. DomAlex J. Smola
    • Srinivas VadrevuChoon Hui TeoSuju RajanKunal PuneraByron E. DomAlex J. Smola
    • G06F17/30
    • G06F16/358G06F16/951
    • Techniques for clustering of search results are described. In an example embodiment, a plurality of first clusters is determined, in a corpus of articles, independently of user queries issued against the corpus of articles, where each first cluster represents a group of articles that relate to a news story. One or more cluster identifiers are assigned to each article in the corpus, where the one or more cluster identifiers respectively identify one or more of the plurality of first clusters to which the article belongs. A query that specifies search criteria against the corpus of articles is received. In response to receiving the query, a result for the query is generated by at least selecting, from the corpus of articles, a set of articles based on the search criteria. The selected set of articles is grouped into one or more second clusters based at least on the one or more cluster identifiers that are assigned to each article in the set of articles. In the result for the query, the set of articles is organized according to the one or more second clusters.
    • 描述搜索结果聚类技术。 在示例实施例中,在文章的语料库中,独立于针对文章语料库的用户查询来确定多个第一群集,其中每个第一群集代表与新闻故事相关的一组文章。 一个或多个集群标识符被分配给语料库中的每个文章,其中一个或多个集群标识符分别标识文章所属的多个第一集群中的一个或多个。 接收到针对文章语料库指定搜索条件的查询。 响应于接收到查询,通过至少从文章的语料库中选择基于搜索条件的一组文章来生成查询的结果。 所选择的一组文章至少基于分配给该组文章中的每个文章的一个或多个集群标识符而被分组成一个或多个第二集群。 在查询的结果中,根据一个或多个第二集群来组织文章集。
    • 10. 发明申请
    • FEATURE NORMALIZATION AND ADAPTATION TO BUILD A UNIVERSAL RANKING FUNCTION
    • 特征正则化和适应性建立通用排名功能
    • US20100293175A1
    • 2010-11-18
    • US12464660
    • 2009-05-12
    • Srinivas VadrevuBelle L. Tseng
    • Srinivas VadrevuBelle L. Tseng
    • G06F17/30G06F15/18
    • G06F16/951
    • To increase the amount of training data available to train a machine learning ranking function, data from multiple markets are normalized in such a manner as to optimize a measurement of quality of the ranking function trained on the various sets of normalized training data. Furthermore, the feature scores of training data from individual markets are adapted to conform to the distributions of feature scores from a base market. Such adapted training data from the various markets may be used to train a single, robust ranking function. Adaptation of feature scores in a particular training data set involves mapping feature scores of the particular training data set to feature scores of a base training data set to conform the distributions of the feature scores in the particular training data set to the distributions of the feature scores in the base training data set.
    • 为了增加可用于训练机器学习排序功能的训练数据的数量,来自多个市场的数据被归一化,以便优化在各种归一化训练数据集上训练的排名函数的质量测量。 此外,来自各个市场的训练数据的特征得分适应于基础市场的特征得分的分布。 可以使用来自各个市场的这种适应的训练数据来训练单个,鲁棒的排名功能。 在特定训练数据集中的特征得分的适应涉及将特定训练数据集的特征得分映射到基本训练数据集的特征分数,以使特定训练数据集中的特征得分的分布符合特征得分的分布 在基础训练数据集中。