聚类算法在多层感知器训练中形成代表性样本的应用

IF 0.2 Q4 PHYSICS, MULTIDISCIPLINARY
Aleksey A. Pastukhov, Aleksander A. Prokofiev
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引用次数: 3

摘要

本文研究了多层感知器(MLP)型神经网络的代表性样本的有效形成问题。提出了一种基于聚类的方法来增加训练集的熵。为了形成具有代表性的样本,对各种聚类算法进行了检验。基于算法对各维度因子空间进行聚类,形成具有代表性的样本。为了验证我们的方法,我们合成了MLP神经网络并对其进行了训练。对聚类和非聚类形成的集合进行训练。针对代表性样本形成问题,对聚类算法的有效性进行了对比分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering algorithms application to forming a representative sample in the training of a multilayer perceptron

In this paper, we have considered the problem of effectively forming the representative sample for training a neural network of the multilayer perceptron (MLP) type. An approach based on the use of clustering that allowed to increase the entropy of the training set was put forward. Various clustering algorithms were examined in order to form the representative sample. The algorithm-based clustering of factor spaces of various dimensions was carried out, and a representative sample was formed. To verify our approach we synthesized the MLP neural network and trained it. The training technique was performed with the sets formed both with and without clustering. A comparative analysis of the effectiveness of clustering algorithms was carried out in relation to the problem of representative sample formation.

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