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公开(公告)号：US20230267351A1
公开(公告)日：2023-08-24
申请号：US17939778
申请日：2022-09-07
申请人： Hitachi, Ltd.
发明人： Hiroyuki NAMBA , Masaki HAMAMOTO , Masashi EGI
CPC分类号： G06N5/045 , G06K9/6235 , G06K2009/6237
摘要： A generation apparatus is configured to access a set of pieces of learning data each being a combination of a value of an explanatory variable and a value of an objective variable, a function family list including, of functions each indicating a physical law and an attribute of each of the functions, at least the functions, and search range limiting information for limiting a search range of the function family list, wherein the processor is configured to execute: first generation processing of generating a first prediction expression by setting a first parameter for the explanatory variable to a first function included in the function family list; first calculation processing of calculating, based on the search range limiting information, a first conviction degree relating to the first prediction expression; and first output processing of outputting the first prediction expression and the first conviction degree.
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公开(公告)号：US20230196562A1
公开(公告)日：2023-06-22
申请号：US17936626
申请日：2022-09-29
申请人： PAIGE.AI, Inc.
发明人： Jillian SUE , Sam SEYMOUR
IPC分类号： G06T7/00 , G06K9/62 , G06V10/764 , G06V10/25 , G16H10/40
CPC分类号： G06T7/0012 , G06K9/6235 , G06V10/765 , G06V10/25 , G16H10/40 , G06K2009/6237 , G06T2207/30168 , G06T2207/30024
摘要： Systems and methods are described herein for processing electronic medical images to optimize a review order of pathology cases. For example, a plurality of variables and one or more constraints may be received along with a plurality of pathology cases. Each case of the plurality of pathology cases may include one or more medical images of at least one pathology specimen associated with a patient. The medical images from each case, the plurality of variables, and the one or more constraints may be provided as input to a trained system. A sequential order for user review of the plurality of cases to optimize one or more of the plurality of variables based on the one or more constraints may be received as output of the trained system. Each case of the plurality of cases may be automatically provided to a user for review according to the sequential order.
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公开(公告)号：US20150117766A1
公开(公告)日：2015-04-30
申请号：US14459242
申请日：2014-08-13
CPC分类号： G06N99/005 , G06K9/6235 , G06K2009/6236
摘要： A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.
摘要翻译： 一种用于数据集的特征变换的方法包括：接收包括具有相应类别标签的原始特征样本的数据集; 将数据集分为方向优化集和训练集; 使用方向优化集来计算最大化类间分离性并使特征样本相对于相应类标签的类内方差最小化的最佳变换向量; 使用最佳变换向量将数据集的其余原始特征样本变换为具有增强的鉴别特征的新特征样本; 并使用新的特征样本训练分类器，其中该方法由一个或多个处理器执行。
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公开(公告)号：US06961703B1
公开(公告)日：2005-11-01
申请号：US09660635
申请日：2000-09-13
CPC分类号： G10L19/0204 , G06K9/6235 , G10L17/02 , G10L19/022
摘要： A speech verification process involves comparison of enrollment and test speech data and an improved method of comparing the data is disclosed, wherein segmented frames of speech are analyzed jointly, rather than independently. The enrollment and test speech are both subjected to a feature extraction process to derive fixed-length feature vectors, and the feature vectors are compared, using a linear discriminant analysis and having no dependence upon the order of the words spoken or the speaking rate. The discriminant analysis is made possible, despite a relatively high dimensionality of the feature vectors, by a mathematical procedure provided for finding an eigenvector to simultaneously diagonalize the between-speaker and between-channel covariances of the enrollment and test data.
摘要翻译： 语音验证过程包括比较注册和测试语音数据，并且公开了一种比较数据的改进方法，其中分割的语音帧被共同分析而不是独立地分析。 注册和测试语音都进行特征提取处理以得出固定长度特征向量，并且使用线性判别分析来比较特征向量，并且不依赖于口语的顺序或说话率。 尽管特征向量的维度相对较高，但通过提供用于寻找特征向量以同时使注册和测试数据之间的扬声器之间和频道间协方差同时对角化的数学过程，判别分析成为可能。
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5.发明公开公开(公告)号：US20230259830A1
公开(公告)日：2023-08-17
申请号：US17651293
申请日：2022-02-16
CPC分类号： G06N20/20 , G06K9/6262 , G06K9/6235 , G05B17/02 , G06K2009/6237
摘要： A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.
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公开(公告)号：US20170046597A1
公开(公告)日：2017-02-16
申请号：US15307026
申请日：2014-04-30
发明人： Ming C. Hao , Wei-Nchih Lee , Alexander Jaeger , Nelson L. Chang , Daniel Keim
IPC分类号： G06K9/62
CPC分类号： G06K9/6224 , G06K9/00 , G06K9/6215 , G06K9/6218 , G06K9/6235 , G06K2009/6236
摘要： In an example, high-dimensional data is projected to a multi-dimensional space to differentiate clusters of the high-dimensional data. A user selection of at least two of the clusters may be received and a plurality of dissimilar dimensions may be extracted from the at least two clusters. In addition, a user selected of a dissimilar dimension from the plurality of extracted dissimilar dimensions may be received. In response to receipt of the user selection of the dissimilar dimension from the plurality of dissimilar dimensions, a plurality of correlated dimensions to the dissimilar dimension may be determined. In addition, the plurality of dissimilar dimensions and the plurality of correlated dimensions may be displayed.
摘要翻译： 在一个示例中，高维数据被投影到多维空间以区分高维数据的簇。 可以接收至少两个群集的用户选择，并且可以从至少两个群集中提取多个不同维度。 此外，可以接收从多个提取的不同维度中选择不同维度的用户。 响应于从多个不同维度接收到不同维度的用户选择，可以确定与不相似维度的多个相关维度。 此外，可以显示多个不同维度和多个相关维度。
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公开(公告)号：US08682065B2
公开(公告)日：2014-03-25
申请号：US12344018
申请日：2008-12-24
申请人： Meng Wang , Xian-Sheng Hua
发明人： Meng Wang , Xian-Sheng Hua
IPC分类号： G06K9/00
CPC分类号： G06K9/6235 , G06K9/6215
摘要： This disclosure describes various exemplary systems, computer program products, and methods for feature distance metric learning with feature decomposition (DMLFD). The disclosure describes decomposing a high-dimensional feature space into one or more low-dimensional feature spaces according to minimum dependence. Furthermore, the disclosure describes how the sub-metrics are constructed and combined to form a global metric.
摘要翻译： 本公开描述了具有特征分解（DMLFD）的各种示例性系统，计算机程序产品和用于特征距离度量学习的方法。 本公开描述了根据最小依赖性将高维特征空间分解为一个或多个低维特征空间。 此外，本公开描述如何构建和组合子度量以形成全局度量。
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8.发明申请公开(公告)号：US20050123202A1
公开(公告)日：2005-06-09
申请号：US11002082
申请日：2004-12-03
申请人： Wonjun Hwang , Taekyun Kim
发明人： Wonjun Hwang , Taekyun Kim
CPC分类号： G06K9/6235 , G06K9/00275
摘要： A face recognition apparatus and method using Principal Component Analysis (PCA) learning per subgroup, the face recognition apparatus includes: a learning unit which performs Principal Component Analysis (PCA) learning on each of a plurality of subgroups constituting a training data set, and then performs Linear Discriminant Analysis (LDA) learning on the training data set, thereby generating a PCA-based LDA (PCLDA) basis vector set of each subgroup; a feature vector extraction unit which projects a PCLDA basis vector set of each subgroup to an input image and extracts a feature vector set of the input image with respect to each subgroup; a feature vector storing unit which projects a PCLDA basis vector set of each subgroup to each of a plurality of face images to be registered, thereby generating a feature vector set of each registered image with respect to each subgroup, and storing the feature vector set in a database; and a similarity calculation unit which calculates a similarity between the input image and each registered image.
摘要翻译： 一种面部识别装置和方法，使用每个子组的主成分分析（PCA）学习，面部识别装置包括：学习单元，对构成训练数据集的多个子组中的每一个进行主成分分析（PCA）学习，然后 对训练数据集进行线性判别分析（LDA）学习，从而生成每个子组的基于PCA的LDA（PCLDA）基向量集; 特征向量提取单元，其将每个子组的PCLDA基矢量集投影到输入图像，并且针对每个子组提取输入图像的特征向量集合; 特征矢量存储单元，其将每个子组的PCLDA基矢量集投影到要登记的多个面部图像中的每一个，从而针对每个子组生成每个注册图像的特征向量集合，并将设置在 数据库 以及相似度计算单元，其计算输入图像和每个注册图像之间的相似度。
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公开(公告)号：US20030172284A1
公开(公告)日：2003-09-11
申请号：US10296677
申请日：2002-11-26
发明人： Josef Kittler
IPC分类号： H04K001/00
CPC分类号： G06K9/6235 , G06K9/6284 , G07C9/00087
摘要： A personal identity authentication process and system use a class specific linear discriminant transformation to test authenticity of a probe face image. A nullclient acceptancenull approach, an nullimposter rejectionnull approach and a nullfusednull approach are described.
摘要翻译： 个人身份认证过程和系统使用类别特定的线性判别变换来测试探针面部图像的真实性。 描述了“客户接受”方法，“冒牌拒绝”方法和“融合”方法。
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公开(公告)号：US09471886B2
公开(公告)日：2016-10-18
申请号：US14459242
申请日：2014-08-13
CPC分类号： G06N99/005 , G06K9/6235 , G06K2009/6236
摘要： A method for feature transformation of a data set includes: receiving a data set including original feature samples with corresponding class labels; splitting the data set into a direction optimization set and a training set; using the direction optimization set to calculate an optimum transformation vector that maximizes inter-class separability and minimizes intra-class variance of the feature samples with respect to corresponding class labels; using the optimum transformation vector to transform the rest of the original feature samples of the data set to new feature samples with enhanced discriminative characteristics; and training a classifier using the new feature samples, wherein the method is performed by one or more processors.
摘要翻译： 一种用于数据集的特征变换的方法包括：接收包括具有相应类别标签的原始特征样本的数据集; 将数据集分为方向优化集和训练集; 使用方向优化集来计算最大化类间分离性并使特征样本相对于相应类标签的类内方差最小化的最佳变换向量; 使用最佳变换向量将数据集的其余原始特征样本变换为具有增强的鉴别特征的新特征样本; 并使用新的特征样本训练分类器，其中该方法由一个或多个处理器执行。
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