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Combining Multiple Kernels by Augmenting the Kernel Matrix

机译:通过增强内核矩阵来组合多个内核

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摘要

In this paper we present a novel approach to combining multiple kernels where the kernels are computed from different information channels. In contrast to traditional methods that learn a linear combination of n kernels of size m x m, resulting in m coefficients in the trained classifier, we propose a method that can learn nxm coefficients. This allows to assign different importance to the information channel per example rather than per kernel. We analyse the proposed kernel combination in empirical feature space and provide its geometrical interpretation. We validate the approach on both UCI datasets and an object recognition dataset, and demonstrate that it leads to classification improvements.
机译:在本文中,我们提出了一种新颖的方法来组合多个内核,其中内核是从不同的信息通道计算得出的。与学习学习大小为m x m的n个核的线性组合并在经过训练的分类器中产生m个系数的传统方法相反,我们提出了一种可以学习nxm个系数的方法。这允许每个示例而不是每个内核为信息通道分配不同的重要性。我们在经验特征空间中分析提出的核组合,并提供其几何解释。我们在UCI数据集和对象识别数据集上都验证了该方法,并证明了该方法可以改进分类。

著录项

  • 来源
    《Multiple classifier systems》|2010年|p.175-184|共10页
  • 会议地点 Cairo(EG);Cairo(EG);Cairo(EG)
  • 作者单位

    Centre for Vision, Speech, and Signal Processing University of Surrey Guildford, Surrey, GU2 7XH, UK;

    Centre for Vision, Speech, and Signal Processing University of Surrey Guildford, Surrey, GU2 7XH, UK;

    Centre for Vision, Speech, and Signal Processing University of Surrey Guildford, Surrey, GU2 7XH, UK;

    Centre for Vision, Speech, and Signal Processing University of Surrey Guildford, Surrey, GU2 7XH, UK;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP274.3;
  • 关键词

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