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    • 2. 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。
    • 3. 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。
    • 6. 发明授权
    • System and method for probabilistic relational clustering
    • 概率关系聚类的系统和方法
    • US08285719B1
    • 2012-10-09
    • US12538835
    • 2009-08-10
    • Bo LongZhongfei (Mark) Zhang
    • Bo LongZhongfei (Mark) Zhang
    • G06F7/00G06F17/30
    • G06F17/30598G06N7/005G06N99/005
    • Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such as Web mining, search marketing, bioinformatics, citation analysis, and epidemiology. A probabilistic model is presented for relational clustering, which also provides a principal framework to unify various important clustering tasks including traditional attributes-based clustering, semi-supervised clustering, co-clustering and graph clustering. The model seeks to identify cluster structures for each type of data objects and interaction patterns between different types of objects. Under this model, parametric hard and soft relational clustering algorithms are provided under a large number of exponential family distributions. The algorithms are applicable to relational data of various structures and at the same time unify a number of state-of-the-art clustering algorithms: co-clustering algorithms, the k-partite graph clustering, and semi-supervised clustering based on hidden Markov random fields.
    • 关系聚类由于其在涉及多类型相关数据对象,如Web挖掘,搜索营销,生物信息学,引文分析和流行病学等各种重要应用中的显着影响而引起越来越多的关注。 提出了关系聚类的概率模型,为统一各种重要的聚类任务提供了一个主要框架,包括传统的基于属性的聚类,半监督聚类,共聚类和图聚类。 该模型旨在确定不同类型对象之间的每种类型的数据对象和交互模式的集群结构。 在这个模型下,在大量的指数族分布下提供了参数化的硬和软关系聚类算法。 该算法适用于各种结构的关系数据,同时统一了许多最先进的聚类算法:共聚类算法,k-分块图聚类和基于隐马尔可夫的半监督聚类 随机字段
    • 7. 发明授权
    • Combining multiple clusterings by soft correspondence
    • 通过软对应组合多个集群
    • US08195734B1
    • 2012-06-05
    • US11945956
    • 2007-11-27
    • Bo LongZhongfei Mark Zhang
    • Bo LongZhongfei Mark Zhang
    • G06F7/32
    • G06F17/30598
    • Combining multiple clusterings arises in various important data mining scenarios. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit correspondence between the classes from different clusterings. Provided is a framework based on soft correspondence to directly address the correspondence problem in combining multiple clusterings. Under this framework, an algorithm iteratively computes the consensus clustering and correspondence matrices using multiplicative updating rules. This algorithm provides a final consensus clustering as well as correspondence matrices that gives intuitive interpretation of the relations between the consensus clustering and each clustering from clustering ensembles. Extensive experimental evaluations demonstrate the effectiveness and potential of this framework as well as the algorithm for discovering a consensus clustering from multiple clusterings.
    • 在各种重要的数据挖掘方案中,组合了多个集群。 然而,从多个集群中找到共识聚类是一项具有挑战性的任务,因为不同集群的类之间没有明确的对应关系。 提供了一种基于软对应的框架,直接解决组合多个集群的对应问题。 在这个框架下,算法使用乘法更新规则迭代地计算共享聚类和对应矩阵。 该算法提供最终的一致聚类以及对应矩阵,从而可以直观地解释聚类集合中的共聚集和聚类之间的关系。 广泛的实验评估表明了该框架的有效性和潜力,以及从多个聚类中发现共聚集的算法。
    • 8. 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。
    • 9. 发明授权
    • Focusing mechanism
    • 聚焦机制
    • US08908260B2
    • 2014-12-09
    • US13443249
    • 2012-04-10
    • Bo Long
    • Bo Long
    • G02F1/29G02B15/14G02B7/02G03B17/00G03B3/10G02B7/08
    • G03B3/10G02B7/08
    • A focusing mechanism for focusing a lens module includes a base seat, a movable platform, a positioning assembly, a support bracket and a plurality of arms. The positioning assembly is fixed to the base seat and passes through a center of the movable platform, a lens of the lens module is detachably mounted on the positioning assembly. The support bracket is fixed to the movable platform. A sensor of the lens module is detachably mounted on the support bracket. Each of the plurality of arms rotatably interconnects the movable platform and the base seat, the movable platform drives the support bracket to rotate relative to the positioning assembly to enable the lens to rotate relative to the sensor via a drive of the arms.
    • 用于聚焦透镜模块的聚焦机构包括基座,可移动平台,定位组件,支撑托架和多个臂。 定位组件固定在基座上并穿过可移动平台的中心,透镜模块的透镜可拆卸地安装在定位组件上。 支撑架固定在活动平台上。 透镜模块的传感器可拆卸地安装在支撑支架上。 多个臂中的每一个可旋转地互连可移动平台和基座,可移动平台驱动支撑托架相对于定位组件旋转,以使透镜能够经由臂的驱动相对于传感器旋转。
    • 10. 发明授权
    • Robot arm assembly
    • 机器人臂组件
    • US08839689B2
    • 2014-09-23
    • US12975665
    • 2010-12-22
    • Bo Long
    • Bo Long
    • B25J17/02
    • B25J17/0258Y10S901/29Y10T74/20329Y10T74/20335
    • A robot arm assembly includes a first robot arm and a second robot arm; the second robot arm is rotatably connected to the first robot arm. The first robot arm includes a first sleeve, a first input shaft, and a second input shaft. The first input shaft and the second input shaft are seated in the first sleeve. The second robot arm includes a second sleeve and an output shaft; the output shaft is received in the second sleeve. The first input shaft is connected to the second sleeve via a pair of bevel gears, and drives the second sleeve to swing relative to the first sleeve. The second input shaft is connected to the output shaft via a plurality of bevel gears meshing with each other, and drives the output shaft to rotate relative to the second sleeve.
    • 机器人臂组件包括第一机器人臂和第二机器人臂; 第二机器人臂可旋转地连接到第一机器人手臂。 第一机器人臂包括第一套筒,第一输入轴和第二输入轴。 第一输入轴和第二输入轴位于第一套筒中。 第二机器人臂包括第二套筒和输出轴; 输出轴被接收在第二套筒中。 第一输入轴经由一对锥齿轮连接到第二套筒,并驱动第二套筒相对于第一套筒摆动。 第二输入轴经由多个彼此啮合的锥齿轮连接到输出轴,并且驱动输出轴相对于第二套筒旋转。