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Application of Artificial Metaplasticity fundamentals to WBCD Breast Cancer Database classification method

机译:人工代谢基础知识在WBCD乳腺癌数据库分类方法中的应用

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The Metaplasticity is an inherent property of the Biological neuron connections that consists in the capacity of modifying the learning mechanism using the information present in the network itself during the training. This concept can be applied to Artificial Learning Algorithms using a technique called Artificial Metaplasticity. The idea is to improve the results in Machine Learning taking as the base the hypothesis studied by Metaplasticity in Biological Learning. This paper presents and discuss the results of applying an Artificial Metaplasticity implementation based on the information present at the output of the network in Multilayer Perceptrons at artificial neuron learning level. The objective of this study is a state-of-the-art research: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Database.
机译:代谢性是生物神经元连接的固有属性,其特征在于,可以利用训练过程中网络本身中存在的信息来修改学习机制。可以使用称为“人工超塑性”的技术将此概念应用于人工学习算法。这个想法是要以机器学习的结果为基础,以生物学习中的可塑性来研究假说。本文介绍并讨论了基于人工神经元学习级别的多层感知器网络输出中存在的信息,应用人工可塑性实现的结果。这项研究的目的是进行最先进的研究:威斯康星州乳腺癌数据库对乳腺癌数据的诊断。

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