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1. 发明申请

US20080033899A1 Feature selection method using support vector machine classifier 有权
标题翻译：特征选择方法采用支持向量机分类器
公开(公告)号：US20080033899A1
公开(公告)日：2008-02-07
申请号：US11842934
申请日：2007-08-21
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06N5/02
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： Identification of a determinative subset of features from within a large set of features is performed by training a support vector machine to rank the features according to classifier weights, where features are removed to determine how their removal affects the value of the classifier weights. The features having the smallest weight values are removed and a new support vector machine is trained with the remaining weights. The process is repeated until a relatively small subset of features remain that is capable of accurately separating the data into different patterns or classes. The method is applied for selecting the smallest number of genes that are capable of accurately distinguishing between medical conditions such as cancer and non-cancer.
摘要翻译：通过训练支持向量机来根据分类器权重来对特征的确定性子集进行识别，其中去除特征以确定它们的去除如何影响分类器权重的值。去除具有最小权重值的特征，并用剩余权重训练新的支持向量机。重复该过程直到相对较小的特征子集保持能够将数据精确地分离成不同的模式或类别。该方法适用于选择能够准确区分癌症和非癌症等医学病症的最小数量的基因。

2. 发明授权

US06760715B1 Enhancing biological knowledge discovery using multiples support vector machines 有权
标题翻译：使用多个支持向量机增强生物知识发现
公开(公告)号：US06760715B1
公开(公告)日：2004-07-06
申请号：US09633616
申请日：2000-08-07
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06F1518
CPC分类号： G06K9/6256 , G06F19/20 , G06F19/24 , G06K9/6269 , G06N99/005
摘要： Multiple support vector machines are used to extract useful information from vast quantities of biological data. The method includes pre-processing of training data and test data to add dimensionality or to identify missing or erroneous data points. The training data is used to train the learning machine after which the success of the training is tested using the test data. The test output is pre-processed to determine whether the knowledge discovered from the pre-processed test data set is desirable and to identify which of the multiple support vector machines provides the optimal solution. After the training has been confirmed, live biological data can be pre-processed then input into the identified support vector machine that provides the optimal solution for extraction of useful information.
摘要翻译：多支持向量机用于从大量生物数据中提取有用的信息。该方法包括预处理训练数据和测试数据以增加维度或识别丢失或错误的数据点。训练数据用于训练学习机，之后使用测试数据测试训练的成功。测试输出被预处理以确定从预处理的测试数据集中发现的知识是否是期望的，并且识别多个支持向量机中的哪一个提供最佳解决方案。训练确定后，生物体数据可以预处理，然后输入到识别的支持向量机中，为提取有用信息提供最佳解决方案。

3. 发明申请

US20100256988A1 SYSTEM FOR PROVIDING DATA ANALYSIS SERVICES USING A SUPPORT VECTOR MACHINE FOR PROCESSING DATA RECEIVED FROM A REMOTE SOURCE 有权
标题翻译：使用支持向量机提供数据分析服务的系统，用于处理从远程源获取的数据
公开(公告)号：US20100256988A1
公开(公告)日：2010-10-07
申请号：US12814431
申请日：2010-06-11
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06Q40/00 , G06F15/18 , G06N5/02 , G06Q10/00 , G06Q50/00
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： A network-based system is provided for performing data analysis services using a support vector machine for analyzing data received from a remote user connected to the network. The user transmits a data set to be analyzed and along with an account identifier that allows the analysis service provider to collect payment for the processing services. Once payment has been confirmed, the service provider's server transmits the analysis results to the remote user.
摘要翻译：提供了一种基于网络的系统，用于使用支持向量机执行数据分析服务，用于分析从连接到网络的远程用户接收的数据。用户发送要分析的数据集，以及允许分析服务提供商收集处理服务的支付的帐户标识符。付款确认后，服务提供商的服务器将分析结果发送给远程用户。

4. 发明授权

US07797257B2 System for providing data analysis services using a support vector machine for processing data received from a remote source 失效
标题翻译：用于使用支持向量机提供数据分析服务的系统，用于处理从远程源接收的数据
公开(公告)号：US07797257B2
公开(公告)日：2010-09-14
申请号：US11926129
申请日：2007-10-29
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06N3/00 , G06F15/18
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： A computer system for performing data analysis services using a support vector machine for analyzing data received from a remote source on a distributed network includes a server in communication with the distributed network for receiving a data set and a financial account identifier associated with the remote source. The server communicates over the distributed network with a financial institution to receive funds from a financial account identified by the financial account identifier. A processor receives one or more data sets from the remote source and pre-processes the data to enhance meaning within the data set. The pre-processed data is used to train and test a support vector machine for recognizing patterns within the data. Live data is processed using the trained and tested support vector machine to generate an output which is transmitted to the remote source after the server confirms that payment for the data processing service has been received.
摘要翻译：用于使用支持向量机执行数据分析服务的计算机系统用于分析从分布式网络上的远程源接收的数据，包括与分布式网络通信的服务器，用于接收与远程源相关联的数据集和财务帐户标识符。服务器通过分布式网络与金融机构进行通信，从金融账户标识符识别的金融账户接收资金。处理器从远程源接收一个或多个数据集，并且预处理数据以增强数据集内的含义。预处理数据用于训练和测试支持向量机，用于识别数据内的模式。使用经过训练和测试的支持向量机处理实时数据，以产生在服务器确认已经接收到数据处理服务的支付之后被发送到远程源的输出。

5. 发明申请

US20050165556A1 Colon cancer biomarkers 审中-公开
标题翻译：结肠癌生物标志物
公开(公告)号：US20050165556A1
公开(公告)日：2005-07-28
申请号：US11033570
申请日：2005-01-11
申请人： Stephen Barnhill , Jason Weston , Isabelle Guyon
发明人： Stephen Barnhill , Jason Weston , Isabelle Guyon
IPC分类号： G06F15/18 , G06F19/00 , G06K9/62 , G06Q10/06 , G06Q20/10 , G06Q50/22 , G06T7/00 , G01N33/48 , G01N33/50
CPC分类号： G06K9/623 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N20/00 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , G16B25/00 , G16B40/00 , Y02A90/22 , Y02A90/24 , Y02A90/26
摘要： Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.
摘要翻译：特别是通过使用多学习机器的数据增强知识发现的系统和方法，特别是多个支持向量机。学习机器的训练数据被预先处理，以便增加其意义。每个包含不同内核的多个支持向量机用预处理的训练数据进行训练，并用以相同方式预处理的测试数据进行测试。比较来自多个支持向量机的测试输出，以确定哪个测试输出（如果有的话）代表最优解。可以调整一个或多个内核的选择，并且可以对一个或多个支持向量机进行再培训和再测试。可以组合基于不同输入数据集的最佳解决方案以形成要输入到一个或多个附加支持向量机的新输入数据集。本发明的方法，系统和装置包括使用支持向量机来识别对医学诊断，预后和治疗很重要的模式。这种模式可以在许多不同的数据集中找到。本发明还包括用于治疗和诊断医学病症的方法和组合物。

6. 发明授权

US06789069B1 Method for enhancing knowledge discovered from biological data using a learning machine 有权
标题翻译：使用学习机增强从生物数据中发现的知识的方法
公开(公告)号：US06789069B1
公开(公告)日：2004-09-07
申请号：US09633850
申请日：2000-08-07
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06F1518
CPC分类号： G06K9/6269 , G06F19/20 , G06F19/24 , G06K9/623 , G06N99/005
摘要： A learning machine is used to extract useful information from vast quantities of biological data. The method includes pre-processing of training data and test data to add dimensionality or to identify missing or erroneous data points. The training data is used to train the learning machine after which the success of the training is tested using the test data. The test output is pre-processed to determine whether the knowledge discovered from the pre-processed test data set is desirable. After the training has been confirmed, live biological data can be pre-processed then input into the trained learning machine for extraction of useful information. In the preferred embodiment, the learning machine is one or more support vector machines.
摘要翻译：学习机器用于从大量生物数据中提取有用的信息。该方法包括预处理训练数据和测试数据以增加维度或识别丢失或错误的数据点。训练数据用于训练学习机，之后使用测试数据测试训练的成功。测试输出被预先处理，以确定从预处理的测试数据集中发现的知识是否是可取的。训练确定后，生物体数据可以预先处理，然后输入训练有素的学习机器，以提取有用信息。在优选实施例中，学习机器是一个或多个支持向量机。

7. 发明授权

US07542959B2 Feature selection method using support vector machine classifier 有权
标题翻译：特征选择方法采用支持向量机分类器
公开(公告)号：US07542959B2
公开(公告)日：2009-06-02
申请号：US11842934
申请日：2007-08-21
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06N3/08 , G06K9/62
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： Identification of a determinative subset of features from within a large set of features is performed by training a support vector machine to rank the features according to classifier weights, where features are removed to determine how their removal affects the value of the classifier weights. The features having the smallest weight values are removed and a new support vector machine is trained with the remaining weights. The process is repeated until a relatively small subset of features remain that is capable of accurately separating the data into different patterns or classes. The method is applied for selecting the smallest number of genes that are capable of accurately distinguishing between medical conditions such as cancer and non-cancer.
摘要翻译：通过训练支持向量机来根据分类器权重来对特征的确定性子集进行识别，其中去除特征以确定它们的去除如何影响分类器权重的值。去除具有最小权重值的特征，并用剩余权重训练新的支持向量机。重复该过程直到相对较小的特征子集保持能够将数据精确地分离成不同的模式或类别。该方法适用于选择能够准确区分癌症和非癌症等医学病症的最小数量的基因。

8. 发明申请

US20080059392A1 SYSTEM FOR PROVIDING DATA ANALYSIS SERVICES USING A SUPPORT VECTOR MACHINE FOR PROCESSING DATA RECEIVED FROM A REMOTE SOURCE 失效
标题翻译：使用支持向量机提供数据分析服务的系统，用于处理从远程源获取的数据
公开(公告)号：US20080059392A1
公开(公告)日：2008-03-06
申请号：US11926129
申请日：2007-10-29
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06N3/00
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： A computer system for performing data analysis services using a support vector machine for analyzing data received from a remote source on a distributed network includes a server in communication with the distributed network for receiving a data set and a financial account identifier associated with the remote source. The server communicates over the distributed network with a financial institution to receive funds from a financial account identified by the financial account identifier. A processor receives one or more data sets from the remote source and pre-processes the data to enhance meaning within the data set. The pre-processed data is used to train and test a support vector machine for recognizing patterns within the data. Live data is processed using the trained and tested support vector machine to generate an output which is transmitted to the remote source after the server confirms that payment for the data processing service has been received.
摘要翻译：用于使用支持向量机执行数据分析服务的计算机系统用于分析从分布式网络上的远程源接收的数据，包括与分布式网络通信的服务器，用于接收与远程源相关联的数据集和财务帐户标识符。服务器通过分布式网络与金融机构进行通信，从金融账户标识符识别的金融账户接收资金。处理器从远程源接收一个或多个数据集，并且预处理数据以增强数据集内的含义。预处理数据用于训练和测试支持向量机，用于识别数据内的模式。使用经过训练和测试的支持向量机处理实时数据，以产生在服务器确认已经接收到数据处理服务的支付之后被发送到远程源的输出。

9. 发明授权

US06882990B1 Methods of identifying biological patterns using multiple data sets 失效
标题翻译：使用多个数据集识别生物模式的方法
公开(公告)号：US06882990B1
公开(公告)日：2005-04-19
申请号：US09633410
申请日：2000-08-07
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06F15/18 , G06F19/00 , G06K9/62 , G06Q10/06 , G06Q20/10 , G06Q50/22 , G06T7/00 , G06F1/00
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.
摘要翻译：特别是通过使用多学习机器的数据增强知识发现的系统和方法，特别是多个支持向量机。学习机器的训练数据被预先处理，以便增加其意义。每个包含不同内核的多个支持向量机用预处理的训练数据进行训练，并用以相同方式预处理的测试数据进行测试。比较来自多个支持向量机的测试输出，以确定哪个测试输出（如果有的话）代表最优解。可以调整一个或多个内核的选择，并且可以对一个或多个支持向量机进行再培训和再测试。可以组合基于不同输入数据集的最佳解决方案以形成要输入到一个或多个附加支持向量机的新输入数据集。本发明的方法，系统和装置包括使用支持向量机来识别对医学诊断，预后和治疗很重要的模式。这种模式可以在许多不同的数据集中找到。本发明还包括用于治疗和诊断医学病症的方法和组合物。

10. 发明授权

US06714925B1 System for identifying patterns in biological data using a distributed network 有权
标题翻译：使用分布式网络识别生物数据模式的系统
公开(公告)号：US06714925B1
公开(公告)日：2004-03-30
申请号：US09633627
申请日：2000-08-07
申请人： Stephen Barnhill , Isabelle Guyon , Jason Weston
发明人： Stephen Barnhill , Isabelle Guyon , Jason Weston
IPC分类号： G06F1700
CPC分类号： G06K9/623 , G06F19/20 , G06F19/24 , G06K9/6228 , G06K9/6231 , G06K9/6256 , G06K9/6269 , G06N99/005 , G06Q10/0637 , G06Q20/10 , G06Q40/06 , G06Q50/22 , G06T7/0012 , Y02A90/22 , Y02A90/26
摘要： System for enhancing knowledge discovery from data using a learning machine in general and a support vector machine in particular in a distributed network environment. A customer transmits training data, test data and live data to a vendor's server from a remote source, via a distributed network. The training biological data, test biological data and live biological data is stored in a storage device. Training biological data is then pre-processed in order to add meaning thereto. Pre-processing data involves transforming the biological data points and/or expanding the biological data points. Live biological data is pre-processed and input into the trained and tested learning machine. The live output from the learning machine is then post-processed into a computationally derived alphanumerical classifier for interpretation by a human or computer automated process.
摘要翻译：用于通过使用学习机器的数据增强知识发现的系统以及特别是在分布式网络环境中的支持向量机。客户通过分布式网络从远程源将培训数据，测试数据和实时数据传输到供应商的服务器。训练生物数据，测试生物数据和活体生物数据存储在存储设备中。然后对培训生物数据进行预处理，以便增加其含义。预处理数据涉及转化生物数据点和/或扩展生物数据点。活体生物数据经过预处理并输入到受过训练和测试的学习机器中。然后将来自学习机的实时输出后处理成计算导出的字母数字分类器，用于人或计算机自动化过程的解释。

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