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    Spread Kernel Support Vector Machine
    1.
    发明申请
    Spread Kernel Support Vector Machine 有权
    扩展内核支持向量机

    公开(公告)号：US20070094170A1

    公开(公告)日：2007-04-26

    申请号：US11276235

    申请日：2006-02-20

    Applicant: Hans Graf ,  Igor Durdanovic ,  Eric Cosatto ,  Vladimir Vapnik

    Inventor： Hans Graf ,  Igor Durdanovic ,  Eric Cosatto ,  Vladimir Vapnik

    IPC: G06N3/02

    CPC classification number: G06K9/6269 ,  G06N99/005

    Abstract: Disclosed is a parallel support vector machine technique for solving problems with a large set of training data where the kernel computation, as well as the kernel cache and the training data, are spread over a number of distributed machines or processors. A plurality of processing nodes are used to train a support vector machine based on a set of training data. Each of the processing nodes selects a local working set of training data based on data local to the processing node, for example a local subset of gradients. Each node transmits selected data related to the working set (e.g., gradients having a maximum value) and receives an identification of a global working set of training data. The processing node optimizes the global working set of training data and updates a portion of the gradients of the global working set of training data. The updating of a portion of the gradients may include generating a portion of a kernel matrix. These steps are repeated until a convergence condition is met. Each of the local processing nodes may store all, or only a portion of, the training data. While the steps of optimizing the global working set of training data, and updating a portion of the gradients of the global working set, are performed in each of the local processing nodes, the function of generating a global working set of training data is performed in a centralized fashion based on the selected data (e.g., gradients of the local working set) received from the individual processing nodes.

    Abstract translation: 公开了一种用于解决大量训练数据的问题的并行支持向量机技术，其中内核计算以及内核高速缓存和训练数据分布在多个分布式机器或处理器上。 多个处理节点用于基于一组训练数据训练支持向量机。 每个处理节点基于处理节点本地的数据，例如梯度的本地子集，选择训练数据的本地工作集。 每个节点发送与工作集有关的所选数据（例如，具有最大值的梯度）并且接收训练数据的全局工作集合的标识。 处理节点优化训练数据的全局工作集，并更新全局训练数据工作集的一部分梯度。 梯度的一部分的更新可以包括生成内核矩阵的一部分。 重复这些步骤直到满足收敛条件。 每个本地处理节点可以存储训练数据的全部或仅一部分。 虽然在每个本地处理节点中执行优化训练数据的全局工作集和更新全局工作集的一部分梯度的步骤，但是在每个本地处理节点中执行生成训练数据的全局工作集的功能， 基于从各个处理节点接收的所选数据（例如，本地工作集的梯度）的集中式。

    Spread kernel support vector machine
    2.
    发明授权
    Spread kernel support vector machine 有权
    扩展内核支持向量机

    公开(公告)号：US07406450B2

    公开(公告)日：2008-07-29

    申请号：US11276235

    申请日：2006-02-20

    Applicant: Hans Peter Graf ,  Igor Durdanovic ,  Eric Cosatto ,  Vladimir Vapnik

    Inventor： Hans Peter Graf ,  Igor Durdanovic ,  Eric Cosatto ,  Vladimir Vapnik

    IPC: G06E1/00 ,  G06E3/00 ,  G06F15/18 ,  G06G7/00 ,  G06N3/02

    CPC classification number: G06K9/6269 ,  G06N99/005

    Abstract: Disclosed is a parallel support vector machine technique for solving problems with a large set of training data where the kernel computation, as well as the kernel cache and the training data, are spread over a number of distributed machines or processors. A plurality of processing nodes are used to train a support vector machine based on a set of training data. Each of the processing nodes selects a local working set of training data based on data local to the processing node, for example a local subset of gradients. Each node transmits selected data related to the working set (e.g., gradients having a maximum value) and receives an identification of a global working set of training data. The processing node optimizes the global working set of training data and updates a portion of the gradients of the global working set of training data. The updating of a portion of the gradients may include generating a portion of a kernel matrix. These steps are repeated until a convergence condition is met. Each of the local processing nodes may store all, or only a portion of, the training data. While the steps of optimizing the global working set of training data, and updating a portion of the gradients of the global working set, are performed in each of the local processing nodes, the function of generating a global working set of training data is performed in a centralized fashion based on the selected data (e.g., gradients of the local working set) received from the individual processing nodes.

    Abstract translation: 公开了一种用于解决大量训练数据的问题的并行支持向量机技术，其中内核计算以及内核高速缓存和训练数据分布在多个分布式机器或处理器上。 多个处理节点用于基于一组训练数据训练支持向量机。 每个处理节点基于处理节点本地的数据，例如梯度的本地子集，选择训练数据的本地工作集。 每个节点发送与工作集有关的所选数据（例如，具有最大值的梯度）并且接收训练数据的全局工作集合的标识。 处理节点优化训练数据的全局工作集，并更新全局训练数据工作集的一部分梯度。 梯度的一部分的更新可以包括生成内核矩阵的一部分。 重复这些步骤直到满足收敛条件。 每个本地处理节点可以存储训练数据的全部或仅一部分。 虽然在每个本地处理节点中执行优化训练数据的全局工作集和更新全局工作集的一部分梯度的步骤，但是在每个本地处理节点中执行生成训练数据的全局工作集的功能， 基于从各个处理节点接收的所选数据（例如，本地工作集的梯度）的集中式。

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