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首页> 外文会议>International Conference on Computer, Communications, and Control Technology >Feature extraction using neocognitron learning in Hierarchical Temporary Memory
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Feature extraction using neocognitron learning in Hierarchical Temporary Memory

机译：分层临时记忆中使用新认知学习的特征提取

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摘要

Hierarchical Temporal Memory (HTM) serves as a practical implementation of the memory prediction theory. In order to obtain the optimum accuracy in pattern recognition, it is crucial to apply an appropriate learning algorithm for the feature extraction step of the HTM. This study proposes the use of neocognitron learning in extracting features of the pattern for image recognition. The integration of neocognitron into HTM addresses both the scale and time issues of the HTM. As for evaluation, a comparison is made against the original HTM and principal component analysis (PCA). The results show that more features are extracted as a function of input patterns than the original HTM and PCA.
机译：分层时间记忆（HTM）是记忆预测理论的一种实际实现。为了在模式识别中获得最佳精度，至关重要的是为HTM的特征提取步骤应用适当的学习算法。这项研究提出了使用新认知加速器学习来提取图像特征的特征。将新认知激素整合到HTM中解决了HTM的规模和时间问题。至于评估，将与原始HTM和主成分分析（PCA）进行比较。结果表明，与原始HTM和PCA相比，根据输入模式提取的特征更多。

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