首页> 外文会议>International Conference on Neural Information Processing;ICONIP 2007 >Effectiveness of Scale Free Network to the Performance Improvement of a Morphological Associative Memory without a Kernel Image
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Effectiveness of Scale Free Network to the Performance Improvement of a Morphological Associative Memory without a Kernel Image

机译:无尺度网络对无核图像形态联想记忆性能改进的有效性

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In this paper, we present a new approach of the morphological associative memory (MAM) without a kernel image to reduce the network size by using the scale free network. The MAM is one of the powerful associative memories compared to ordinary associative memories. Weak point of the MAM is to need the kernel image which is susceptibility to noise and hard to design. We have already presented the MAM without a kernel image as a practical model. However the model has a drawback that the perfect recall rate is degraded. On the other hand, it has been reported that an introduction of the scale free network to associative memories is effective in the improvement of the recall rate and the reduction of the network size. We try to reduce the network size and improve the recall rate by introducing the scale free network.
机译:在本文中,我们提出了一种新的不使用内核映像的形态联想记忆(MAM)的方法,以通过使用无标度网络来减小网络大小。与普通联想存储器相比,MAM是强大的联想存储器之一。 MAM的弱点是需要容易受噪声影响且难以设计的内核映像。我们已经提出了没有内核映像的MAM作为实用模型。但是,该模型的缺点是完美召回率会下降。另一方面,据报道,将无标度网络引入关联存储器对于提高召回率和减小网络规模是有效的。我们尝试通过引入无标度网络来减小网络规模并提高召回率。

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