分享兴趣，传播快乐，

增长见闻，留下美好。

亲爱的您，

这里是LearingYard学苑！

今天小编为大家带来《基于ACO-SVM算法的供应链风险预警研究》精读。

欢迎您的用心访问！

本期推文阅读时长大约5分钟，请您耐心阅读。

Share interest, spread happiness,

increase knowledge, and leave beautiful.

Dear you,

this is the LearingYard Academy!

Today, the editor brings intensive reading of

"Research on Supply Chain Risk Early Warning Based on ACO-SVM algorithm".

Welcome your visit!

The reading time of this tweet is about 5 minutes, please read it with patience.

今天小编将从思维导图、精读内容、知识补充三个板块为大家带来《基于ACO-SVM算法的供应链风险预警研究》支持向量机原理。

Today, I will bring you "A Study on Supply Chain Risk Early Warning Based on ACO-SVM Algorithm" support vector machine principles from three sections of mind map, intensive reading content and knowledge supplement.

01思维导图

02精读内容

一、基本支持向量机模型

1. Basic support vector machine model

支持向量机基本思想：

Basic ideas of support vector machines:

二、支持向量机回归模型

2. Support vector machine regression model

03知识补充

文中提到的核函数是什么？

What are the kernel functions mentioned in the text?

支持向量机中常用的核函数：

Kernel functions commonly used in support vector machines:

支持向量回归基本思路：

Support vector regression basic idea:

今天的分享就到这里了。

如果您对今天的文章有独特的想法,

欢迎给我们留言，

让我们相约明天。

祝您今天过得开心快乐！

That's all for today's sharing.

If you have a unique idea for today’s article,

please leave us a message,

and let us meet tomorrow.

I wish you a nice day !

参考资料：Deepl、CSDN、百度

参考文献：

[1]张振强. 基于ACO-SVM算法的供应链风险预警研究 [D]. 福州大学, 2016.

本文由LearningYard学苑原创，如有侵权请在后台留言！

文字|Zheng

排版|Zheng

审核|wang