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11. 发明申请

US20100318482A1 Kernels for Identifying Patterns in Datasets Containing Noise or Transformation Invariances 有权
标题翻译：用于识别包含噪声或转换入数的数据集中的模式的内核
公开(公告)号：US20100318482A1
公开(公告)日：2010-12-16
申请号：US12868658
申请日：2010-08-25
申请人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
发明人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets include an invariance transformation or noise, tangent vectors are defined to identify relationships between the invariance or noise and the training data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel, which may be based on a kernel PCA map.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集包括不变性变换或噪声的情况下，定义向量以识别不变性或噪声与训练数据点之间的关系。使用正切向量形成协方差矩阵，然后用于生成内核，其可以基于内核PCA映射。

12. 发明授权

US07788193B2 Kernels and methods for selecting kernels for use in learning machines 失效
标题翻译：内核和选择用于学习机器的内核的方法
公开(公告)号：US07788193B2
公开(公告)日：2010-08-31
申请号：US11929354
申请日：2007-10-30
申请人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
发明人： Peter L. Bartlett , André Elisseeff , Bernhard Schoelkopf , Olivier Chapelle
IPC分类号： G06F15/18 , G06F17/00 , G06N5/00
CPC分类号： G06K9/623 , G06F19/24 , G06K9/6215 , G06K9/6248 , G06K9/6269 , G06N99/005 , G06Q20/042
摘要： Learning machines, such as support vector machines, are used to analyze datasets to recognize patterns within the dataset using kernels that are selected according to the nature of the data to be analyzed. Where the datasets possesses structural characteristics, locational kernels can be utilized to provide measures of similarity among data points within the dataset. The locational kernels are then combined to generate a decision function, or kernel, that can be used to analyze the dataset. Where an invariance transformation or noise is present, tangent vectors are defined to identify relationships between the invariance or noise and the data points. A covariance matrix is formed using the tangent vectors, then used in generation of the kernel.
摘要翻译：使用学习机器（如支持向量机）分析数据集，以使用根据要分析的数据的性质选择的内核来识别数据集中的模式。在数据集具有结构特征的情况下，可以利用位置内核提供数据集中的数据点之间的相似度度量。然后组合位置内核以生成可用于分析数据集的决策函数或内核。在存在不变变换或噪声的情况下，定义向量以识别不变性或噪声与数据点之间的关系。使用切向矢量形成协方差矩阵，然后用于生成内核。

13. 发明授权

US07565409B2 Acquiring web page information without commitment to downloading the web page 有权
标题翻译：无需承诺下载网页即可获取网页信息
公开(公告)号：US07565409B2
公开(公告)日：2009-07-21
申请号：US11564093
申请日：2006-11-28
申请人： Lisa Heilbron , John C. Platt , Patrice Y. Simard , Bernhard Schoelkopf
发明人： Lisa Heilbron , John C. Platt , Patrice Y. Simard , Bernhard Schoelkopf
IPC分类号： G06F15/16
CPC分类号： G06F17/30905 , Y10S707/99933 , Y10S707/99936
摘要： What is disclosed is acquiring information regarding a web page, without having to commit to downloading that page. In one embodiment, after a current web page is downloaded from one source, and information regarding web pages linked to by links in the current web page are downloaded from a second source, when a user hovers a cursor over a link on a current web page, an informational region is displayed by the link that includes the information from the second source. The informational region may include, for example, a text box that apparently floats by the link. The information in the region can include, for example, keywords in the meta tags of the web page; paragraph headings of the web page; links on the web page to other pages; etc.
摘要翻译：公开的是获取关于网页的信息，而不必承诺下载该页面。在一个实施例中，在从一个源下载当前网页之后，从第二来源下载与当前网页中的链接相链接的网页的信息，当用户将光标悬停在当前网页上的链接上时，信息区域由包括来自第二源的信息的链接显示。信息区域可以包括例如显然由链接浮动的文本框。该区域中的信息可以包括例如网页的元标签中的关键字; 网页的段落标题; 网页链接到其他页面; 等等

14. 发明申请

US20050104966A1 Interactive images 有权
标题翻译：交互式图像
公开(公告)号：US20050104966A1
公开(公告)日：2005-05-19
申请号：US11020943
申请日：2004-12-22
申请人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
发明人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
IPC分类号： G06K9/00 , G06T11/00 , H04N5/225
CPC分类号： G06T11/001
摘要： A system and process for creating an interactive digital image, which allows a viewer to interact with a displayed image so as to change it with regard to a desired effect, such as exposure, focus or color, among others. An interactive image includes representative images which depict a scene with some image parameter varying between them. The interactive image also includes an index image, whose pixels each identify the representative image that exhibits the desired effect related to the varied image parameter at a corresponding pixel location. For example, a pixel of the index image might identify the representative image having a correspondingly-located pixel that depicts a portion of the scene at the sharpest focus. One primary form of interaction involves selecting a pixel of a displayed image whereupon the representative image identified in the index image at a corresponding pixel location is displayed in lieu of the currently displayed image.
摘要翻译：一种用于创建交互式数字图像的系统和过程，其允许观看者与显示的图像交互，以便根据期望的效果（例如曝光，聚焦或颜色等）改变它。交互式图像包括描绘具有在它们之间变化的一些图像参数的场景的代表性图像。交互式图像还包括索引图像，其像素每个标识在相应像素位置呈现与变化的图像参数相关的期望效果的代表图像。例如，索引图像的像素可以识别具有对应位置的像素的代表图像，其描绘最尖锐焦点的场景的一部分。一种主要的交互形式涉及选择显示图像的像素，由此显示在相应像素位置处的索引图像中标识的代表图像来代替当前显示的图像。

15. 发明授权

US07318051B2 Methods for feature selection in a learning machine 有权
标题翻译：学习机器中特征选择的方法
公开(公告)号：US07318051B2
公开(公告)日：2008-01-08
申请号：US10478192
申请日：2002-05-20
申请人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schoelkopf , Fernando Pérez-Cruz
发明人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schoelkopf , Fernando Pérez-Cruz
IPC分类号： G06F15/18 , G06E1/00 , G06E3/00
CPC分类号： G06K9/6215 , G06K9/623 , G06K9/6231 , G06K9/6256 , G06K9/6262 , G06K9/6269 , G06N99/005 , G09B5/00 , G09B7/00
摘要： In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (lo-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection. (FIG. 3, 300, 301, 302, 304, 306, 308, 309, 310, 311, 312, 314)
摘要翻译：在训练学习机之前的预处理步骤中，预处理包括使用从递归特征消除（RFE）中选出的特征选择方法来减少要处理的特征量的数量，使非零参数的数量最小化的系统（最小化），评估成本函数以识别与由学习集施加的约束兼容的特征的子集，不平衡相关得分和转换特征选择。然后，特征选择之后剩余的特征用于训练学习机，用于模式分类，回归，聚类和/或新颖性检测。（图3），300,301,302,304,306,308,309,310,311,312,314,314，

16. 发明申请

US20070106956A1 ACQUIRING WEB PAGE INFORMATION WITHOUT COMMITMENT TO DOWNLOADING THE WEB PAGE 有权
标题翻译：无需承诺下载网页即可获取网页信息
公开(公告)号：US20070106956A1
公开(公告)日：2007-05-10
申请号：US11564093
申请日：2006-11-28
申请人： John Platt , Lisa Heilbron , Patrice Simard , Bernhard Schoelkopf
发明人： John Platt , Lisa Heilbron , Patrice Simard , Bernhard Schoelkopf
IPC分类号： G06F7/00
CPC分类号： G06F17/30905 , Y10S707/99933 , Y10S707/99936
摘要： What is disclosed is acquiring information regarding a web page, without having to commit to downloading that page. In one embodiment, after a current web page is downloaded from one source, and information regarding web pages linked to by links in the current web page are downloaded from a second source, when a user hovers a cursor over a link on a current web page, an informational region is displayed by the link that includes the information from the second source. The informational region may include, for example, a text box that apparently floats by the link. The information in the region can include, for example, keywords in the meta tags of the web page; paragraph headings of the web page; links on the web page to other pages; etc.
摘要翻译：公开的是获取关于网页的信息，而不必承诺下载该页面。在一个实施例中，在从一个源下载当前网页之后，从第二来源下载与当前网页中的链接相链接的网页的信息，当用户将光标悬停在当前网页上的链接上时，信息区域由包括来自第二源的信息的链接显示。信息区域可以包括例如显然由链接浮动的文本框。该区域中的信息可以包括例如网页的元标签中的关键字; 网页的段落标题; 网页链接到其他页面; 等等

17. 发明申请

US20050216426A1 Methods for feature selection in a learning machine 有权
标题翻译：学习机器中特征选择的方法
公开(公告)号：US20050216426A1
公开(公告)日：2005-09-29
申请号：US10478192
申请日：2002-05-20
申请人： Jason Aaron Weston , Andre Elisseeff , Bernhard Schoelkopf , Fernando Perez-Cruz
发明人： Jason Aaron Weston , Andre Elisseeff , Bernhard Schoelkopf , Fernando Perez-Cruz
IPC分类号： G06F15/18 , G09B5/00 , G09B7/00
CPC分类号： G06K9/6215 , G06K9/623 , G06K9/6231 , G06K9/6256 , G06K9/6262 , G06K9/6269 , G06N99/005 , G09B5/00 , G09B7/00
摘要： In a pre-processing step prior to training a learning machine, pre-processing includes reducing the quantity of features to be processed using feature selection methods selected from the group consisting of recursive feature elimination (RFE), minimizing the number of non-zero parameters of the system (lo-norm minimization), evaluation of cost function to identify a subset of features that are compatible with constraints imposed by the learning set, unbalanced correlation score and transductive feature selection. The features remaining after feature selection are then used to train a learning machine for purposes of pattern classification, regression, clustering and/or novelty detection.
摘要翻译：在训练学习机之前的预处理步骤中，预处理包括使用从递归特征消除（RFE）中选出的特征选择方法来减少要处理的特征量的数量，使非零参数的数量最小化的系统（最小化），评估成本函数以识别与由学习集施加的约束兼容的特征的子集，不平衡相关得分和转换特征选择。然后，特征选择之后剩余的特征用于训练学习机，用于模式分类，回归，聚类和/或新颖性检测。

18. 发明申请

US20050104969A1 Interactive images 有权
公开(公告)号：US20050104969A1
公开(公告)日：2005-05-19
申请号：US11020926
申请日：2004-12-22
申请人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
发明人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
IPC分类号： G06K9/00 , G06T11/00 , H04N5/225
CPC分类号： G06T11/001
摘要： A system and process for creating an interactive digital image, which allows a viewer to interact with a displayed image so as to change it with regard to a desired effect, such as exposure, focus or color, among others. An interactive image includes representative images which depict a scene with some image parameter varying between them. The interactive image also includes an index image, whose pixels each identify the representative image that exhibits the desired effect related to the varied image parameter at a corresponding pixel location. For example, a pixel of the index image might identify the representative image having a correspondingly-located pixel that depicts a portion of the scene at the sharpest focus. One primary form of interaction involves selecting a pixel of a displayed image whereupon the representative image identified in the index image at a corresponding pixel location is displayed in lieu of the currently displayed image.

19. 发明授权

US07970718B2 Method for feature selection and for evaluating features identified as significant for classifying data 有权
标题翻译：用于特征选择和评估对分类数据有重要意义的特征的方法
公开(公告)号：US07970718B2
公开(公告)日：2011-06-28
申请号：US12890705
申请日：2010-09-26
申请人： Isabelle Guyon , Andre Elisseeff , Bernhard Schoelkopf , Jason Aaron Edward Weston , Fernando Perez-Cruz
发明人： Isabelle Guyon , Andre Elisseeff , Bernhard Schoelkopf , Jason Aaron Edward Weston , Fernando Perez-Cruz
IPC分类号： G06F15/18
CPC分类号： G06F19/24 , G06F19/20 , G06K9/6231
摘要： A group of features that has been identified as “significant” in being able to separate data into classes is evaluated using a support vector machine which separates the dataset into classes one feature at a time. After separation, an extremal margin value is assigned to each feature based on the distance between the lowest feature value in the first class and the highest feature value in the second class. Separately, extremal margin values are calculated for a normal distribution within a large number of randomly drawn example sets for the two classes to determine the number of examples within the normal distribution that would have a specified extremal margin value. Using p-values calculated for the normal distribution, a desired p-value is selected. The specified extremal margin value corresponding to the selected p-value is compared to the calculated extremal margin values for the group of features. The features in the group that have a calculated extremal margin value less than the specified margin value are labeled as falsely significant.
摘要翻译：使用支持向量机将资源分为类别的“特征”组合进行评估，该支持向量机将数据集一次分为一个特征。分离后，基于第一类中最低特征值与第二类中最高特征值之间的距离，为每个特征分配极值边缘值。另外，对于两个类别的大量随机绘制的示例集合中的正态分布计算极值边界值，以确定具有指定的极值边界值的正态分布内的示例的数量。使用为正态分布计算的p值，选择所需的p值。对应于所选择的p值的指定极值余量值与所计算的特征组的极值边际值进行比较。计算的极值余量值小于指定余量值的组中的特征被标记为错误显着。

20. 发明授权

US07444016B2 Interactive images 有权
公开(公告)号：US07444016B2
公开(公告)日：2008-10-28
申请号：US11272548
申请日：2005-11-09
申请人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
发明人： Bernhard Schoelkopf , Kentaro Toyama , Matthew Uyttendaele
IPC分类号： G06K9/00 , G06T15/00 , G06T17/00
CPC分类号： G06T11/001
摘要： A system and process for creating an interactive digital image, which allows a viewer to interact with a displayed image so as to change it with regard to a desired effect, such as exposure, focus or color, among others. An interactive image includes representative images which depict a scene with some image parameter varying between them. The interactive image also includes an index image, whose pixels each identify the representative image that exhibits the desired effect related to the varied image parameter at a corresponding pixel location. For example, a pixel of the index image might identify the representative image having a correspondingly-located pixel that depicts a portion of the scene at the sharpest focus. One primary form of interaction involves selecting a pixel of a displayed image whereupon the representative image identified in the index image at a corresponding pixel location is displayed in lieu of the currently displayed image.

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