Abstract

It is proposed to use the degrees of membership of objects to each class in the process of recognition in the linear corrector model to solve the problem of restoring dependences from precedent samples. Two models of the algorithm for calculating estimates are used as classifiers. The work of the proposed model is compared with the original method and with the well-known data analysis methods. The dependence of the work of the linear corrector on its parameters is studied.

中文翻译：

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基于监督学习的分类问题集和隶属度的解的预测

摘要

提出了在线性校正器模型的识别过程中使用对象对每个类的隶属度来解决从先前样本恢复依赖性的问题。用于计算估计值的算法的两个模型用作分类器。将该模型的工作与原始方法和著名的数据分析方法进行了比较。研究了线性校正器的工作对其参数的依赖性。