• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of machine learning and cybernetics >Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms
【24h】

Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms

机译：通过一类神经网络对大脑活动的认知状态进行分类，并通过遗传算法进行特征选择

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

It is generally assumed that one-class machine learning techniques can not reach the performance level of two-class techniques. The importance of this work is that while one-class is often the appropriate classification setting for identifying cognitive brain functions, most work in the literature has focused on two-class methods. In this paper, we demonstrate how one-class recognition of cognitive brain functions across multiple subjects can be performed at the 90% level of accuracy via an appropriate choice of features which can be chosen automatically. Our work extends one-class work by Hardoon and Manevitz (fMRI analysis via one-class machine learning techniques. In: Proceedings of the Nineteenth IJCAI, pp 1604-1605, 2005), where such classification was first shown to be possible in principle albeit with an accuracy of about 60%. The results of this paper are also comparable to work of various groups around the world e.g. Cox and Savoy (NeuroImage 19:261-270, 2003), Mourao-Miranda et al. (NeuroImage, 2006) and Mitchell et al., (Mach Learn 57:145-175, 2004) which have concentrated on two-class classification. The strengthening in the feature selection was accomplished by the use of a genetic algorithm run inside the context of a wrapper approach around a compression neural network for the basic one-class identification. In addition, versions of one-class SVM due to Scholkopf et al. (Estimating the support of a high-dimensional distribution. Technical Report MSR-TR-99-87, Microsoft Research, 1999) and Manevitz and Yousef (J Mach Learn Res 2:139-154, 2001) were investigated.
机译：通常假定一类机器学习技术不能达到两类技术的性能水平。这项工作的重要性在于，尽管一类通常是识别认知脑功能的适当分类设置，但文献中的大多数工作都集中在两类方法上。在本文中，我们演示了如何通过适当选择可以自动选择的功能，以90％的准确度对多个对象进行一类认知脑功能的识别。我们的工作扩展了Hardoon和Manevitz的一类工作（通过一类机器学习技术进行的fMRI分析。在：第19届IJCAI会议录，第1604-1605页，2005年），尽管原则上首次证明可以进行这种分类准确度约为60％。本文的结果也可以与世界各地不同小组的工作相媲美。 Cox and Savoy（NeuroImage 19：261-270，2003），Mourao-Miranda等。 （NeuroImage，2006）和Mitchell等人（Mach Learn 57：145-175，2004）集中在两类分类上。特征选择的增强是通过使用遗传算法在压缩神经网络周围的包装器方法中运行的遗传算法来完成的，以进行基本的一类识别。此外，由于Scholkopf等人，提供了一类SVM的版本。 （估计高维分布的支持。技术报告MSR-TR-99-87，Microsoft Research，1999）以及Manevitz和Yousef（J Mach Learn Res 2：139-154，2001）进行了调查。

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