论文标题
二进制分类器的合奏结合使用复发相关性记忆
Ensemble of Binary Classifiers Combined Using Recurrent Correlation Associative Memories
论文作者
论文摘要
合奏方法应巧妙地结合一组基本分类器，以产生改进的分类器。大多数投票是用于将分类器组合在合奏方法中的方法的示例。在本文中，我们建议使用关联内存模型组合分类器。确切地说，我们基于二进制分类问题的复发相关联想记忆（RCAM）引入集合方法。我们表明，基于RCAM的集合分类器可以被视为多数投票分类器，其权重取决于基本分类器和由此产生的集合方法之间的相似性。更确切地说，基于RCAM的合奏通过经常性咨询和投票计划将分类器结合在一起。此外，计算实验证实了基于RCAM的集合方法在二元分类问题中的潜在应用。
An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.
