Supervised Machine Learning: A Review of Classification Techniques论文摘要（翻译：Trey；审校：Shooya）

摘要：有监督学习是对一类算法的研究，这类算法从表面实例推断出一般性的假说，得出的假说又可以进一步用来预测未发生的实例。换言之，有监督学习的目标，就是根据预测特征，为分类标签的概率分布建立简洁的模型。得到的分类器会被用于为测试实例贴上分类标签，其中，测试实例的分类特征是已知的，但其分类标签是未知的。本文讨论各种有监督机器学习分类技术。当然，单独一篇文章，无法遍及所有的有监督机器学习分类算法（亦称为归纳分类算法），然而我们希望我们所引用的文献可以覆盖主要的理论主题，为正在有趣的研究方向上进行研究的研究者提供指引，并为一些可能的，目前尚未被研究的研究偏好的组合，提出提议。

原文页面：https://s3-us-west-2.amazonaws.com/mlsurveys/54.pdf

Supervised Machine Learning: A Review of Classification
Techniques论文摘要（翻译：Trey；审校：Shooya）

时间： 2024-08-07 15:35:37

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