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31. 发明授权

US07548856B2 Systems and methods for discriminative density model selection 有权
标题翻译：用于区分密度模型选择的系统和方法
公开(公告)号：US07548856B2
公开(公告)日：2009-06-16
申请号：US10441470
申请日：2003-05-20
申请人： Bo Thiesson , Christopher A. Meek
发明人： Bo Thiesson , Christopher A. Meek
IPC分类号： G10L15/06
CPC分类号： G06K9/6226 , G06K9/6296 , G10L15/08
摘要： The present invention utilizes a discriminative density model selection method to provide an optimized density model subset employable in constructing a classifier. By allowing multiple alternative density models to be considered for each class in a multi-class classification system and then developing an optimal configuration comprised of a single density model for each class, the classifier can be tuned to exhibit a desired characteristic such as, for example, high classification accuracy, low cost, and/or a balance of both. In one instance of the present invention, error graph, junction tree, and min-sum propagation algorithms are utilized to obtain an optimization from discriminatively selected density models.
摘要翻译：本发明利用鉴别密度模型选择方法来提供可用于构建分类器的优化密度模型子集。通过允许在多类分类系统中为每个类别考虑多个替代密度模型，然后开发由每个类别的单个密度模型组成的最佳配置，分类器可以被调谐以呈现期望的特性，例如，分类精度高，成本低，和/或两者的平衡。在本发明的一个实例中，使用误差图，结树和最小和传播算法来从区分选择的密度模型中获得优化。

32. 发明授权

US07539690B2 Data mining method and system using regression clustering 有权
标题翻译：数据挖掘方法和系统采用回归聚类
公开(公告)号：US07539690B2
公开(公告)日：2009-05-26
申请号：US10694367
申请日：2003-10-27
申请人： Bin Zhang
发明人： Bin Zhang
IPC分类号： G06F7/00 , G06F17/00
CPC分类号： G06F17/30705 , G06F17/30943 , G06K9/6226 , Y10S707/99937 , Y10S707/99942 , Y10S707/99943
摘要： A method and a system are provided which regressively cluster datapoints from a plurality of data sources without transferring data between the plurality of data sources. In addition, a method and a system are provided which mine data from a dataset by iteratively applying a regression algorithm and a K-Harmonic Means performance function on a set number of functions derived from the dataset.
摘要翻译：提供了一种方法和系统，其从多个数据源回归地聚集数据点，而不在多个数据源之间传送数据。另外，提供了一种方法和系统，其通过对从数据集导出的设定数量的函数迭代地应用回归算法和K谐波均值性能函数来从数据集中挖掘数据。

33. 发明授权

US07536371B2 Apparatus and method for the analysis of a process having parameter-based faults 失效
标题翻译：用于分析具有参数故障的过程的装置和方法
公开(公告)号：US07536371B2
公开(公告)日：2009-05-19
申请号：US11633455
申请日：2006-12-05
申请人： Jehuda Hartman , Eyal Brill , Yuri Kokolov
发明人： Jehuda Hartman , Eyal Brill , Yuri Kokolov
IPC分类号： G06F17/00 , G06N5/02
CPC分类号： G05B23/024 , G05B23/0248 , G05B23/0272 , G06K9/6226 , G06K9/6284 , G06Q10/06
摘要： An apparatus for the analysis of a process having parameter-based faults includes: a parameter value inputer configured for inputting values of at least one process parameter, a fault detector, configured for detecting the occurrence of a fault, a learning file creator associated with the parameter value inputer and the fault detector, configured for separating the input values into a first learning file and a second learning file, the first learning file comprising input values from a collection period preceding each of the detected faults, and the second learning file comprising input values input outside the collection periods, and a learning file analyzer associated with the learning file creator, configured for performing a separate statistical analysis of the first and second learning files, thereby to asses a process status.
摘要翻译：一种用于分析具有基于参数的故障的处理的装置包括：参数值输入器，被配置为输入至少一个过程参数的值，被配置为检测故障发生的故障检测器，与该故障相关联的学习文件创建器参数值输入器和故障检测器，被配置为将输入值分离成第一学习文件和第二学习文件，第一学习文件包括来自检测到的每个故障之前的收集周期的输入值，以及包括输入的第二学习文件在收集周期之外输入的值以及与学习文件创建者相关联的学习文件分析器，被配置为执行第一和第二学习文件的单独的统计分析，从而评估处理状态。

34. 发明申请

US20090006038A1 SOURCE SEGMENTATION USING Q-CLUSTERING 有权
标题翻译：使用Q-CLUSTERING的资源分类
公开(公告)号：US20090006038A1
公开(公告)日：2009-01-01
申请号：US11769928
申请日：2007-06-28
申请人： Nebojsa Jojic , Manuel Jesus Reyes-Gomez
发明人： Nebojsa Jojic , Manuel Jesus Reyes-Gomez
IPC分类号： G06F15/00
CPC分类号： G10L15/04 , G06K9/6226 , G10L21/0272 , G10L25/78
摘要： A system and method that facilitates and effectuates accurate source segmentation of multi-dimensional signals in a computationally efficient manner. By employing Queyranne's algorithm along with a model for combining adjacent multidimensional elements of signal into locally consistent regions, significant improvement in time to identify an optimal segmentation can be achieved. Additional, by saving values computed when executing the algorithm and recalling the values when needed during subsequent iterations of the algorithm provides an additional in algorithm execution speed.
摘要翻译：一种以计算有效的方式促进并实现多维信号的准确源分割的系统和方法。通过采用Queyranne的算法以及将相邻的信号多维元素组合成本地一致区域的模型，可以实现显着改善时间以识别最佳分割。另外，通过保存在执行算法时计算出的值，并且在算法的后续迭代期间需要时调用值提供了额外的算法执行速度。

35. 发明授权

US07299135B2 Methods for identifying discrete populations (e.g., clusters) of data within a flow cytometer multi-dimensional data set 失效
标题翻译：用于识别流式细胞仪多维数据集中的数据的离散群体（例如，簇）的方法
公开(公告)号：US07299135B2
公开(公告)日：2007-11-20
申请号：US11271316
申请日：2005-11-10
申请人： Edward Thayer
发明人： Edward Thayer
IPC分类号： G01N33/48 , G01N33/50
CPC分类号： G01N15/1459 , G01N2015/008 , G01N2015/1006 , G01N2015/1402 , G01N2015/1477 , G06F19/00 , G06K9/00147 , G06K9/6226 , G06K9/626 , G16H50/20 , G16H50/70
摘要： Systems and methods for identifying populations of events in a multi-dimensional data set are described. The populations may, for example, be sets or clusters of data representing different white blood cell components in sample processed by a flow cytometer. The methods use a library consisting of one or more one finite mixture models, each model representing multi-dimensional Gaussian probability distributions with a density function for each population of events expected in the data set. The methods further use an expert knowledge set including one or more data transformations and one or more logical statements. The transformations and logical statements encode a priori expectations as to the populations of events in the data set. The methods further use program code by which a computer may operate on the data, a finite mixture model selected from the library, and the expert knowledge set to thereby identify populations of events in the data set.
摘要翻译：描述用于识别多维数据集中的事件群体的系统和方法。例如，种群可以是由流式细胞仪处理的样品中代表不同白细胞组分的数据集或簇。该方法使用由一个或多个有限混合模型组成的库，每个模型表示数据集中预期的每个事件群的密度函数的多维高斯概率分布。所述方法还使用包括一个或多个数据变换和一个或多个逻辑语句的专家知识集。变换和逻辑语句编码了对数据集中的事件群体的先验期望。这些方法还使用计算机可以对数据进行操作的程序代码，从库中选择的有限混合模型和专家知识集合，从而识别数据集中的事件群体。

36. 发明授权

US07246048B2 Determining near-optimal block size for incremental-type expectation maximization (EM) algorithms 有权
标题翻译：确定增量型期望最大化（EM）算法的近似最优块大小
公开(公告)号：US07246048B2
公开(公告)日：2007-07-17
申请号：US11177734
申请日：2005-07-08
申请人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
发明人： Bo Thiesson , Christopher A. Meek , David E. Heckerman
IPC分类号： G06F7/60
CPC分类号： G06K9/6226 , G06F17/10 , G06F17/18 , Y10S707/99942
摘要： Determining the near-optimal block size for incremental-type expectation maximization (EM) algorithms is disclosed. Block size is determined based on the novel insight that the speed increase resulting from using an incremental-type EM algorithm as opposed to the standard EM algorithm is roughly the same for a given range of block sizes. Furthermore, this block size can be determined by an initial version of the EM algorithm that does not reach convergence. For a current block size, the speed increase is determined, and if the speed increase is the greatest determined so far, the current block size is set as the target block size. This process is repeated for new block sizes, until no new block sizes can be determined.
摘要翻译：公开了确定增量型期望最大化（EM）算法的近似最小块大小。基于新的认识来确定块大小，即对于给定的块大小范围，使用增量型EM算法而不是标准EM算法导致的速度增加大致相同。此外，该块大小可以由未达到收敛的EM算法的初始版本来确定。对于当前块大小，确定速度增加，并且如果到目前为止确定的速度增加最大，则将当前块大小设置为目标块大小。对于新的块大小重复此过程，直到不能确定新的块大小。

37. 发明授权

US07245767B2 Method and apparatus for object identification, classification or verification 失效
标题翻译：用于物体识别，分类或验证的方法和装置
公开(公告)号：US07245767B2
公开(公告)日：2007-07-17
申请号：US10645084
申请日：2003-08-21
申请人： Pedro J. Moreno , Purdy Ho
发明人： Pedro J. Moreno , Purdy Ho
IPC分类号： G06K9/62
CPC分类号： G06K9/6226
摘要： There is provided a method for classifying, identifying or verifying an object by representing the object by a respective sequence of vectors, modeling the sequence of vectors with a respective generative model such that the object is represented by the generative model, computing the distances between the generative models to form one or many kernel matrices based on the distance metric, and using the kernel matrices to classify, identify or verify the object. There is provided a system for determining a classification of an object having a representation module for representing an object by a respective sequence of vectors, a modeling module for modeling the sequence of vectors with a respective generative model such that the object is represented by the generative model, a distance computing module for calculating the distances between the generative models to form one or many kernel matrices based on the distance metric, and a determination module for classifying, identifying or verifying the object based on the kernel matrices.
摘要翻译：提供了一种用于通过用相应的向量序列表示对象来分类，识别或验证对象的方法，用相应的生成模型建模向量序列，使得对象由生成模型表示，计算生成模型基于距离度量形成一个或多个核心矩阵，并使用内核矩阵来分类，识别或验证对象。提供了一种用于确定具有用于通过相应的向量序列表示对象的表示模块的对象的分类的系统，用于使用相应的生成模型对向量序列进行建模的建模模块，使得该对象由生成模型，用于基于距离度量计算生成模型之间的距离以形成一个或多个核心矩阵的距离计算模块，以及用于基于所述核心矩阵对所述对象进行分类，识别或验证的确定模块。

38. 发明授权

US07142693B2 Method of grading sample 失效
标题翻译：样品分级方法
公开(公告)号：US07142693B2
公开(公告)日：2006-11-28
申请号：US10192143
申请日：2002-07-10
申请人： Yupeng Zhang , Christopher K. Shofner , Frederick M. Shofner
发明人： Yupeng Zhang , Christopher K. Shofner , Frederick M. Shofner
IPC分类号： G06K9/00
CPC分类号： G06K9/4652 , G01N21/8851 , G01N33/362 , G06K9/342 , G06K9/38 , G06K9/6226 , G06T7/0006 , G06T7/11 , G06T7/143 , G06T7/62 , G06T7/90 , G06T2207/10016 , G06T2207/20084 , G06T2207/30124
摘要： Method of grading a sample. An input color image of the sample, such as a sample of raw cotton, is obtained by a scanning technique. This image is first analyzed by an image segmentation module and a binary image where pixels of a first characteristic are marked as 0 (e.g. cotton lint) and non-lint pixels are marked as 1. In a following particle recognition module, the adjacent non-lint pixels are grouped together as particles, and the color, size, shape and edge strength for each particle are computed. The particle descriptions are analyzed and each particle is assigned a trash type, i.e. leaf, bark, grass, pepper trash or shadow. The data of leaf and pepper trash are analyzed by a leaf grading module and leaf grade is reported. The data of bark/grass are analyzed by a bark/grass grading module and the bark/grass grade is reported.
摘要翻译：样品分级方法通过扫描技术获得样品的输入彩色图像，例如原棉样品。该图像首先由图像分割模块和第一特征的像素被标记为0（例如棉绒）的二进制图像分析，并且非绒毛像素被标记为1.在随后的颗粒识别模块中，棉绒像素作为粒子分组在一起，并计算每个粒子的颜色，大小，形状和边缘强度。分析颗粒描述，并且每个颗粒被分配为垃圾类型，即叶，树皮，草，胡椒垃圾或阴影。叶片和胡椒垃圾的数据通过叶片分级模块进行分析，报道叶品位。通过树皮/草分级模块分析树皮/草的数据，并报告树皮/草坪。

39. 发明授权

US07080063B2 Probabilistic model generation 有权
公开(公告)号：US07080063B2
公开(公告)日：2006-07-18
申请号：US10424837
申请日：2003-04-29
申请人： Marcos M. Campos , Boriana L. Milenova
发明人： Marcos M. Campos , Boriana L. Milenova
IPC分类号： G06F17/30 , G06F17/10
CPC分类号： G06F17/30601 , G06F17/3061 , G06F2216/03 , G06K9/6223 , G06K9/6226 , Y10S707/99932 , Y10S707/99943
摘要： A database management system provides the capability to perform cluster analysis and provides improved performance in model building and data mining, good integration with the various databases throughout the enterprise, and flexible specification and adjustment of the models being built, but which provides data mining functionality that is accessible to users having limited data mining expertise and which provides reductions in development times and costs for data mining projects. The database management system for in-database clustering, comprises a first data table and a second data table, each data table including a plurality of rows of data, means for building a clustering model using the first data table, means for building a probabilistic model using the clustering model, and means for applying the probabilistic model using the second data table to generate apply output data.

40. 发明授权

US07027643B2 Systems and methods for automatic scale selection in real-time imaging 有权
公开(公告)号：US07027643B2
公开(公告)日：2006-04-11
申请号：US10345131
申请日：2003-05-15
申请人： Dorin Comaniciu , Visvanathan Ramesh
发明人： Dorin Comaniciu , Visvanathan Ramesh
IPC分类号： G06K9/00
CPC分类号： G06K9/42 , G06K9/6226 , G06K9/6298 , G06K2209/01 , G06T7/13 , G06T7/90 , G06T2207/20016 , G06T2207/20048
摘要： A system and method for automatic scale selection in real-time image and video processing and computer vision applications. In one aspect, a non-parametric variable bandwidth mean shift technique, which is based on adaptive estimation of a normalized density gradient, is used for detecting one or more modes in the underlying data and clustering the underlying data. In another aspect, a data-driven bandwidth (or scale) selection technique is provided for the variable bandwidth mean shift method, which estimates for each data point the covariance matrix that is the most stable across a plurality of scales. The methods can be used for detecting modes and clustering data for various types of data such as image data, video data speech data, handwriting data, etc.

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