Wilt Dataset-based Comparative Analysis of Three Neural Networks

Ion Panfilii, R. Precup, Raul-Cristian Roman, E. Petriu
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Abstract

This paper carries out a comparative analysis of three neural networks and their specific kernel machine learning algorithms, namely Inverse Weighted K-means in Online mode (IWKO1), evolving Fuzzy Optimally Pruned Extreme Learning Machine (eF-OP-ELM) and Support Vector Machine (SVM). The presentation of IWKO1, eF-OP-ELM and SVM is given and associated with their mathematical background, and the steps of their implementations are next outlined. The comparative analysis involves two performance indices, prediction accuracy and execution time, measured after the implementation on the Wilt dataset. The analysis is important in the context of the need to handle big data volumes in terms of using special algorithms, which are specially designed to process appropriately such data and information.
基于Wilt数据集的三种神经网络的比较分析
本文对比分析了三种神经网络及其具体的核心机器学习算法,即在线模式下的逆加权k -均值(IWKO1)、进化模糊最优修剪极限学习机(eF-OP-ELM)和支持向量机(SVM)。给出了IWKO1、eF-OP-ELM和SVM的介绍,并与它们的数学背景相关联,接下来概述了它们的实现步骤。对比分析涉及两个性能指标,预测精度和执行时间,在Wilt数据集上实现后测量。在需要使用特殊算法来处理大数据量的背景下,分析是重要的,这些算法是专门设计用于适当处理此类数据和信息的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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