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在深层辐射基函数网络中学习

Fabian Wurzberger¹, Friedhelm Schwenker¹

¹Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany.

Entropy (Basel, Switzerland)

|May 24, 2024

概括

深射线基函数 (RBF) 网络可以使其稳定和高效,用于像图像分类这样的任务. 本研究引入了用于训练更深层次的RBF架构的新方法,实现了与卷积神经网络 (CNN) 相似的结果.

科学领域:

人工智能的人工智能
机器学习机器学习
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

辐射基函数 (RBF) 网络通常是单层的,因为在更深层架构中感知到不稳定性.
现有的通用近似定理支持单层RBF网络,限制了对更深层模型的探索.
包括卷积神经网络 (CNN) 在内的深度神经网络在各种任务中取得了重大成功.

研究的目的:

展示设计稳定,多层RBF网络架构的可行性和有效性.
为深度RBF网络开发高效的学习方案.
将深度RBF网络的性能与已建立的深度学习模型 (如CNN) 进行比较.

主要方法:

引入了深度RBF网络的新型初始化方案,使用k-means集群和协差估计.
利用卷积运算以加快Mahalanobis距离计算,以一种部分连接的方式,受CNNs的启发.
评估了关于图像分类和语音情感识别数据集的拟议深度RBF网络方法.

主要成果:

深度RBF网络成功地设计了高效的学习方案.
提出的方法使多层RBF架构的稳定训练成为可能.
深度RBF网络在测试任务中实现了与CNN等最先进的深度神经网络相提并论的性能.

结论:

可以有效地实现和训练更深层次的RBF网络架构.
开发的初始化和卷积加速技术有助于稳定和高效的深度RBF学习.
深度RBF网络为其他深度学习架构提供了可行的替代方案,用于复杂的任务,例如图像分类和语音情感识别.

关键词:

马哈拉诺比斯是距离的距离这是分类分类的分类.函数的近似函数的近似函数.函数的插值是函数的插值.部分连接的神经网络.辐射基础功能网络辐射基础功能网络

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