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公开(公告)号：US20110119213A1
公开(公告)日：2011-05-19
申请号：US12957411
申请日：2010-12-01
IPC分类号： G06F15/18
CPC分类号： G06F19/24 , G06F19/20 , G06K9/6231
摘要： Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes.
摘要翻译： 通过使用具有类标签的训练样本来训练支持向量机来确定特征组中的特征的确定性子集来确定每个特征的值，其中基于其特征被去除。 删除具有最小值的一个或多个特征，并且使用其余特征生成更新的内核矩阵。 重复该过程，直到保持能够将数据精确地分离成不同类别的预定数量的特征。
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公开(公告)号：US20110106735A1
公开(公告)日：2011-05-05
申请号：US12944197
申请日：2010-11-11
IPC分类号： G06F15/18
CPC分类号： G06K9/6231 , G06N20/00 , G06N20/10 , G16B25/00 , G16B40/00
摘要： Identification of a determinative subset of features from within a group of features is performed by training a support vector machine using training samples with class labels to determine a value of each feature, where features are removed based on their the value. One or more features having the smallest values are removed and an updated kernel matrix is generated using the remaining features. The process is repeated until a predetermined number of features remain which are capable of accurately separating the data into different classes. In some embodiments, features are eliminated by a ranking criterion based on a Lagrange multiplier corresponding to each training sample.
摘要翻译： 通过使用具有类标签的训练样本来训练支持向量机来确定特征组中的特征的确定性子集来确定每个特征的值，其中基于其特征被去除。 删除具有最小值的一个或多个特征，并且使用其余特征生成更新的内核矩阵。 重复该过程，直到保持能够将数据精确地分离成不同类别的预定数量的特征。 在一些实施例中，通过基于对应于每个训练样本的拉格朗日乘数的排序标准来消除特征。
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公开(公告)号：US20050216426A1
公开(公告)日：2005-09-29
申请号：US10478192
申请日：2002-05-20
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。
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公开(公告)号：US20050131847A1
公开(公告)日：2005-06-16
申请号：US10494876
申请日：2002-11-07
CPC分类号： G06K9/623 , G06K9/6269 , G06N99/005
摘要： Features are preprocessed (204) to minimize classification error in a Support Vector Machines (200) used to identify patterns in large databases. Pre-processing (204) is performed to constrain features used to train (210) the SVM learning machine. Live data (226) is collected and processed (232) with SVM.
摘要翻译： 特征是预处理（204），以最小化用于识别大型数据库中的模式的支持向量机（200）中的分类错误。 执行预处理（204）以约束用于训练（210）SVM学习机器的特征。 使用SVM收集和处理实时数据（226）（232）。
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公开(公告)号：US20200074073A1
公开(公告)日：2020-03-05
申请号：US16557416
申请日：2019-08-30
摘要： Embodiments of the invention provide a system including a first logic module for receiving a data stream that includes at least one neural network configured to generate at least one first password sample based at least in part on at least a portion of the data stream. A second logic module can be operatively coupled to the first logic module to receive the first password sample and at least one input dataset including a second password sample. The system can perform calculations to distinguish between at least one password of the first password sample and at least one password of the second password sample. Further, the system can iteratively learn and produce a feedback dataset based on the calculations, where the feedback dataset is configured to be provided to the first logic module.
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6.发明申请METHOD FOR FEATURE SELECTION AND FOR EVALUATING FEATURES IDENTIFIED AS SIGNIFICANT FOR CLASSIFYING DATA 有权特征选择和评估对于分类数据有重要意义的特征的方法公开(公告)号：US20110078099A1
公开(公告)日：2011-03-31
申请号：US12890705
申请日：2010-09-26
申请人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schöelkopf , Fernando Perez-Cruz , Isabelle Guyon
发明人： Jason Aaron Edward Weston , André Elisseeff , Bernhard Schöelkopf , Fernando Perez-Cruz , Isabelle Guyon
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。
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7.发明授权Method for feature selection in a support vector machine using feature ranking 失效使用特征排序的支持向量机中特征选择的方法公开(公告)号：US07805388B2
公开(公告)日：2010-09-28
申请号：US11928784
申请日：2007-10-30
IPC分类号： G06N7/00
CPC分类号： C12Q1/6883 , C12Q2600/112 , C12Q2600/158 , G06F19/20 , G06F19/24 , G06K9/6231 , G06K9/6269
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 (l0-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, transductive feature selection and single feature using margin-based ranking. 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 （10-norm minimization），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，转换特征选择和使用基于边缘的排名的单个特征。 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。
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公开(公告)号：US07475048B2
公开(公告)日：2009-01-06
申请号：US10494876
申请日：2002-11-07
IPC分类号： G06F15/18
CPC分类号： G06K9/623 , G06K9/6269 , G06N99/005
摘要： A computer-implemented method is provided for ranking features within a large dataset containing a large number of features according to each feature's ability to separate data into classes. For each feature, a support vector machine separates the dataset into two classes and determines the margins between extremal points in the two classes. The margins for all of the features are compared and the features are ranked based upon the size of the margin, with the highest ranked features corresponding to the largest margins. A subset of features for classifying the dataset is selected from a group of the highest ranked features. In one embodiment, the method is used to identify the best genes for disease prediction and diagnosis using gene expression data from micro-arrays.
摘要翻译： 提供了一种计算机实现的方法，用于根据每个特征将数据分离成类的能力，对包含大量特征的大型数据集中的特征进行排名。 对于每个特征，支持向量机将数据集分为两类，并确定两类极值点之间的边距。 比较所有功能的边距，并根据边距的大小对特征进行排名，排名最高的功能对应于最大的边距。 从一组最高排名的特征中选择用于分类数据集的特征的子集。 在一个实施方案中，该方法用于使用来自微阵列的基因表达数据鉴定用于疾病预测和诊断的最佳基因。
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9.发明申请METHOD FOR FEATURE SELECTION IN A SUPPORT VECTOR MACHINE USING FEATURE RANKING 失效使用特征排序在支持向量机中选择特征的方法公开(公告)号：US20080233576A1
公开(公告)日：2008-09-25
申请号：US11928784
申请日：2007-10-30
CPC分类号： C12Q1/6883 , C12Q2600/112 , C12Q2600/158 , G06F19/20 , G06F19/24 , G06K9/6231 , G06K9/6269
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 (l0-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, transductive feature selection and single feature using margin-based ranking. 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 （1 0 0 - 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，转换特征选择和单个特征使用 基于边际的排名。 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。
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公开(公告)号：US20080215513A1
公开(公告)日：2008-09-04
申请号：US11929213
申请日：2007-10-30
IPC分类号： G06F15/18
CPC分类号： G06K9/6231 , G06N99/005
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 (l0-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 系统的最小化（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。
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