Analysis of Precision and Accuracy in a Simple Model of Machine Learning

被引：1

作者：

Lee, Julian ^{[1
]}

机构：

[1] Soongsil Univ, Dept Bioinformat & Life Sci, Seoul 06978, South Korea

来源：

JOURNAL OF THE KOREAN PHYSICAL SOCIETY | 2017年 / 71卷 / 12期

基金：

新加坡国家研究基金会;

关键词：

Machine Learning; Regression; Inference Methods; DEEP NEURAL-NETWORKS; PREDICTION;

D O I：

10.3938/jkps.71.866

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Machine learning is a procedure where a model for the world is constructed from a training set of examples. It is important that the model should capture relevant features of the training set, and at the same time make correct prediction for examples not included in the training set. I consider the polynomial regression, the simplest method of learning, and analyze the accuracy and precision for different levels of the model complexity.

引用

页码：866 / 870

页数：5

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