基于时序相关矩阵记忆的人脸特征分类

Nimish Shah
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引用次数: 0

摘要

本文研究了Shah在作者2004年和2006年RASC会议论文[1,2]中介绍的动态编码器(用于二进制神经网络)的动机和概念。此外,本文扩展了Shah等人在IJCNN2007会议论文[3]中关于动态编码器的主张,并对使用动态编码器提供了不同的理解。此外,本文还通过实际考虑(而不是理论概念)导出了改进的相关矩阵记忆(CMML)(首先由Shah等人介绍),提供了一个定理,该定理提供了使用改进形容词的缺失理由,然后最终将CMML增强为“改进的”时间相关矩阵记忆(TCMML),并简要讨论了识别面部特征的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facial Features Classification Using the Temporal Correlation Matrix Memory (TCMML)
This paper examines the motivation and concepts of dynamic encoders (for binary neural networks) introduced by Shah in the author's RASC 2004 and 2006 conference papers [1,2]. Further to this, the paper extends the claims made by Shah et al. in their IJCNN2007 conference paper [3] about dynamic encoders and offers a different understanding to using dynamic encoders. In addition the paper also derives the Improved Correlation Matrix Memory (CMML) (first introduced by Shah et al.) via practical considerations (as opposed to a theoretical concept), supplies a theorem that provides the missing justification over the use of the improved adjective, before finally enhancing the CMML into the `Improved' Temporal Correlation Matrix Memory (TCMML) together with a brief discussion on an application for recognising facial features.
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