Learning in networks: Complex-valued neurons, pruning, and rule extraction

J. Zurada, I. Aizenberg, M. Mazurowski
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引用次数: 3

Abstract

This paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-valued perceptron networks. Learning of CV layers is discussed in context of traditional multilayer feedforward architecture. Such learning is derivative-free and it usually requires networks of reduced size. Selected examples and applications of CV-networks in bioinformatics and pattern recognition are discussed. The paper also covers specialized learning techniques for logic rule extraction. Such techniques include learning with pruning, and can be used in expert systems, and other applications that rely on models developed to fit measured data.
网络中的学习:复值神经元、剪枝和规则提取
本文主要研究具有复值(CV)神经元的神经网络,以及神经网络的学习、修剪和规则提取等方面。CV神经元可以作为实值感知器网络的通用替代品。在传统的多层前馈结构背景下讨论了CV层的学习。这种学习是无导数的,通常需要缩小网络的规模。本文讨论了cv网络在生物信息学和模式识别中的应用。本文还涵盖了逻辑规则提取的专门学习技术。这些技术包括通过修剪进行学习,可以用于专家系统和其他依赖于为拟合测量数据而开发的模型的应用程序。
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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