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1.发明授权Selection of features predictive of biological conditions using protein mass spectrographic data 失效使用蛋白质质谱数据选择预测生物条件的特征公开(公告)号：US07676442B2
公开(公告)日：2010-03-09
申请号：US11929169
申请日：2007-10-30
IPC分类号： G06N5/00
CPC分类号： G06K9/6269 , G06K9/6215 , G06K9/6248
摘要： Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.
摘要翻译： 支持向量机用于对包含在结构化数据集中的数据进行分类，例如由频谱分析仪产生的多个信号。 信号被预处理，以确保谱峰的峰对准。 构建相似性度量以提供用于比较信号样本对的基础。 训练支持向量机以区分不同类别的样本。 以识别光谱中最具预测性的特征。 在优选实施例中，执行特征选择以减少必须考虑的特征的数量。
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公开(公告)号：US07617163B2
公开(公告)日：2009-11-10
申请号：US10267977
申请日：2002-10-09
IPC分类号： G06F15/18
CPC分类号： G06K9/6269 , G06K9/6215 , G06K9/6248
摘要： Support vector machines are used to classify data contained within a structured dataset such as a plurality of signals generated by a spectral analyzer. The signals are pre-processed to ensure alignment of peaks across the spectra. Similarity measures are constructed to provide a basis for comparison of pairs of samples of the signal. A support vector machine is trained to discriminate between different classes of the samples. to identify the most predictive features within the spectra. In a preferred embodiment feature selection is performed to reduce the number of features that must be considered.
摘要翻译： 支持向量机用于对包含在结构化数据集中的数据进行分类，例如由频谱分析仪产生的多个信号。 信号被预处理，以确保谱峰的峰对准。 构建相似性度量以提供用于比较信号样本对的基础。 训练支持向量机以区分不同类别的样本。 以识别光谱中最具预测性的特征。 在优选实施例中，执行特征选择以减少必须考虑的特征的数量。
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3.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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公开(公告)号：US07318051B2
公开(公告)日：2008-01-08
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. (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，
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5.发明申请Identification of Co-Regulation Patterns By Unsupervised Cluster Analysis of Gene Expression Data 失效通过基因表达数据的无监督聚类分析鉴定协调模式公开(公告)号：US20110125683A1
公开(公告)日：2011-05-26
申请号：US13019585
申请日：2011-02-02
申请人： Asa Ben Hur , André Elisseeff , Isabelle Guyon
发明人： Asa Ben Hur , André Elisseeff , Isabelle Guyon
IPC分类号： G06N3/12
CPC分类号： G06F17/30705 , G06F19/20 , G06F19/24 , G06K9/6218
摘要： A method is provided for unsupervised clustering of gene expression data to identify co-regulation patterns. A clustering algorithm randomly divides the data into k different subsets and measures the similarity between pairs of datapoints within the subsets, assigning a score to the pairs based on similarity, with the greatest similarity giving the highest correlation score. A distribution of the scores is plotted for each k. The highest value of k that has a distribution that remains concentrated near the highest correlation score corresponds to the number of co-regulation patterns.
摘要翻译： 提供了用于基因表达数据的无监督聚类以鉴定共调节模式的方法。 聚类算法将数据随机分为k个不同的子集，并测量子集内的数据点对之间的相似度，并根据相似度为该对分配一个分数，最大相似度给出最高相关分数。 为每个k绘制得分的分布。 具有在最高相关分数附近集中的分布的k的最高值对应于协调模式的数量。
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公开(公告)号：US07890445B2
公开(公告)日：2011-02-15
申请号：US11929522
申请日：2007-10-30
申请人： Asa Ben Hur , André Elisseeff , Isabelle Guyon
发明人： Asa Ben Hur , André Elisseeff , Isabelle Guyon
CPC分类号： G06F17/30705 , G06F19/20 , G06F19/24 , G06K9/6218
摘要： A model selection method is provided for choosing the number of clusters, or more generally the parameters of a clustering algorithm. The algorithm is based on comparing the similarity between pairs of clustering runs on sub-samples or other perturbations of the data. High pairwise similarities show that the clustering represents a stable pattern in the data. The method is applicable to any clustering algorithm, and can also detect lack of structure. We show results on artificial and real data using a hierarchical clustering algorithm.
摘要翻译： 提供了一种模型选择方法，用于选择聚类数量，或更一般地选择聚类算法的参数。 该算法基于比较子样本上的聚类运行对与数据的其他扰动之间的相似性。 高成对相似性表明聚类表示数据中的稳定模式。 该方法适用于任何聚类算法，并且还可以检测到结构不足。 我们使用层次聚类算法来显示人造和实际数据的结果。
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7.发明申请Kernels for Identifying Patterns in Datasets Containing Noise or Transformation Invariances 有权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
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。
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公开(公告)号：US07788193B2
公开(公告)日：2010-08-31
申请号：US11929354
申请日：2007-10-30
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。
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9.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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