Acquisition of internal representation by multilayered perceptrons
Acquisition of internal representation by multilayered perceptrons
复制标题
通过多层感知器获取内部表示
DOI：
10.1002/ecjc.4430741111
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发表时间：
1991
期刊：
影响因子：
--
通讯作者：
M. Kawato
中科院分区：
文献类型：
--
作者：
Bunpei Irie;M. Kawato
PDP 模型（具有后向误差传播学习的多层感知器）的特征之一被认为是能够根据描述该映射的输入输出向量样本自动找到输入和输出之间的映射。然而，由于在映射中，代表点的值是通过查表确定的，而其他点的值是通过它们之间的插值确定的，因此可以将其视为基于记忆的推理的一种。另一方面，人们认为PDP模型具有从输入向量中提取特征的能力。
本文对上述两点进行了全面的研究，证明即使输入数据完全相同，当内部表示不同时，也会得到完全不同的结果。规定PDP模型中的信息处理是执行表示最优度量空间（内部表示）中的输入向量分布的变换并对该度量空间进行插值的过程。通过简单的模拟来演示这种内部表示获取能力。
One of the characteristics of the PDP model (multilayer perceptron with backward error propagation learning) is thought to be its ability to automatically find a mapping between inputs and outputs based on input-output vector samples describing that mapping. However, since in the mapping the values of representative points are determined by table lookup, and the values of other points are determined by interpolation between them, it can be viewed as one type of Memory Based Reasoning. On the other hand, it is thought that the PDP model has the ability to extract features from the input vector.
In this paper, the forementioned two points are investigated completely and it is demonstrated that entirely different results are obtained when the internal representation differs, even if the input data are exactly the same. It is stipulated that information processing in the PDP model is a process that carries out a transformation which represents the input vector distribution in an optimal metric space (internal representation) and interpolates that metric space. This internal representation acquisition capability is demonstrated by means of a simple simulation.