An Empirical Study of the Influence of Data Structures on the Performance of VG-RAM Classifiers

Daniel S. F. Alves, D. O. Cardoso, Hugo C. C. Carneiro, F. França, P. Lima
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引用次数: 2

Abstract

This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent VG-RAM related literature, resulting in a novel and accurate yet fast setup.
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数据结构对VG-RAM分类器性能影响的实证研究
本文研究了不同数据结构对基于vg - ram的分类器性能和精度的影响。这种无权重的神经模型基于具有非常大的地址输入的RAM节点,这表明使用特殊的数据结构来处理空间和时间计算成本。研究了四种不同的数据结构,包括最近VG-RAM相关文献中使用的经典数据结构,从而建立了一种新颖,准确而快速的设置。
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