论文标题
特征记忆树
Eigen Memory Trees
论文作者
论文摘要
这项工作介绍了特征记忆树（EMT），这是一种用于顺序学习方案的新型在线存储模型。 EMTS使用以前的经验的主要组成部分将数据存储在二进制树的叶子和新样本中，从而促进对相关记忆的效率高效（对数）访问。我们证明EMT的表现优于现有的在线存储方法，并提供了一种杂交EMT参数算法，该算法比纯粹的参数方法享有极大的提高性能，而几乎没有缺陷。我们的发现是使用OpenML存储库中的206个数据集在有限和无限内存预算情况下进行验证的。
This work introduces the Eigen Memory Tree (EMT), a novel online memory model for sequential learning scenarios. EMTs store data at the leaves of a binary tree and route new samples through the structure using the principal components of previous experiences, facilitating efficient (logarithmic) access to relevant memories. We demonstrate that EMT outperforms existing online memory approaches, and provide a hybridized EMT-parametric algorithm that enjoys drastically improved performance over purely parametric methods with nearly no downsides. Our findings are validated using 206 datasets from the OpenML repository in both bounded and infinite memory budget situations.
