Generation of useful variables and features is an important issue throughout the machine learning, artificial intelligence, and applied fields for their efficient computations. In this paper, the nearest neighbor relations are proposed for the minimal generation and the reduced variables of the functions in the threshold networks. First, the nearest neighbor relations are shown to be minimal and inherited for threshold functions and they play an important role in the iterative generation of the Chow parameters. Further, they give a solution for the Chow parameters problem. Second, convex cones are made of the nearest neighbor relations for the generation of the reduced variables. Then the edges of convex cones are compared for the discrimination of variables. Finally, the reduced variables based on the nearest neighbor relations are shown to be useful for documents classification.

中文翻译：

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通过阈值网络中的最近邻关系生成 Chow 参数和减少变量

有用的变量和特征的生成是整个机器学习、人工智能和应用领域中高效计算的一个重要问题。本文针对阈值网络中函数的最小生成和约简变量提出了最近邻关系。首先，最近邻关系被证明是最小的并且对于阈值函数是继承的，它们在 Chow 参数的迭代生成中起着重要作用。此外，他们给出了 Chow 参数问题的解决方案。其次，凸锥由最近邻关系构成，用于生成减少变量。然后比较凸锥的边缘以区分变量。最后，