多个实例组合用于构建深层异质委员会,用于高维的低样本大小数据.
Qinghua Zhou1, Shuihua Wang1, Hengde Zhu1
1School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK.
概括
本研究介绍了多个实例集群 (MIE),这是一种用于高维,低样本大小领域的深度集群学习的新型堆叠方法. MIE提供了与现有方法相比较的性能,并允许创建可适应的"成长"神经网络级联.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度集体学习通过结合多个模型来提高神经网络的性能.
- 委员会学习,包括神经网络级联,是集体学习中的一个关键领域.
- 高维低样本大小 (HDLS) 领域对传统的机器学习模型提出了独特的挑战.
研究的目的:
- 引入多个实例组合 (MIE) 作为深层组合和级联的新堆叠方法.
- 为了改进特征表示,重新制定集体学习作为多实例学习问题.
- 开发和评估使用MIE的新委员会学习策略,包括增长神经网络级联的概念.
主要方法:
- 组合学习过程被重新定义为多个实例的学习问题.
- 使用聚合运算来关联来自基础神经网络的特征表示.
- 探索注意力机制,并与MIE提出两种新的委员会学习策略.
- 利用MIE的能力来生成伪基神经网络,用于不断增长的级联.
主要成果:
- MIE提供了一类替代组合方法,其性能与现有的堆叠技术相提并论.
- 展示了一种创新方法,用于生成高性能,无限制的"成长"神经网络级联.
- 在多个HDLS数据集中验证了方法,在采用较小样本大小的二进制分类中实现了高性能.
结论:
- MIE是一种有效的堆叠方法,用于深度集体学习,特别是在HDLS环境中.
- 提出的方法提供了一个灵活而强大的框架,用于构建先进的神经网络级联.
- 通过整合多个实例学习原则,MIE为组合学习提供了新的视角.
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