• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Knowledge science, engineering and management >A Competitive Learning Approach to Instance Selection for Support Vector Machines
【24h】

A Competitive Learning Approach to Instance Selection for Support Vector Machines

机译：支持向量机实例选择的竞争学习方法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Support Vector Machines (SVM) have been applied successfully in a wide variety of fields in the last decade. The SVM problem is formulated as a convex objective function subject to box constraints that needs to be maximized, a quadratic programming (QP) problem. In order to solve the QP problem on larger data sets specialized algorithms and heuristics are required. In this paper we present a new data-squashing method for selecting training instances in support vector learning. Inspired by the growing neural gas algorithm and learning vector quantization we introduce a new, parameter robust neural gas variant to retrieve an initial approximation of the training set containing only those samples that will likely become support vectors in the final classifier. This first approximation is refined in the border areas, defined by neighboring neurons of different classes, yielding the final training set. We evaluate our approach on synthetic as well as real-life datasets, comparing run-time complexity and accuracy to a random sampling approach and the exact solution of the support vector machine. Results show that runtime-complexity can be significantly reduced while achieving the same accuracy as the exact solution and that furthermore our approach does not not rely on data set specific parameterization of the sampling rate like random sampling for doing so.
机译：支持向量机（SVM）在过去十年中已成功地应用于许多领域。 SVM问题被公式化为一个凸目标函数，它受到需要最大化的框约束的约束，这是一个二次规划（QP）问题。为了解决较大数据集上的QP问题，需要专门的算法和启发式算法。在本文中，我们提出了一种新的数据压缩方法，用于在支持向量学习中选择训练实例。受不断增长的神经气体算法和学习矢量量化的启发，我们引入了一种新的，参数健壮的神经气体变量，以检索训练集的初始近似值，其中仅包含那些可能会成为最终分类器中支持向量的样本。在由不同类别的相邻神经元定义的边界区域中对第一个近似值进行细化，从而得出最终的训练集。我们对合成数据集和实际数据集进行评估，将运行时的复杂性和准确性与随机抽样方法和支持向量机的精确解决方案进行比较。结果表明，在实现与精确解决方案相同的准确性的同时，可以显着降低运行时复杂度，此外，我们的方法不像随机采样那样依赖于数据集特定的采样率参数设置。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号