Regularization method(正则化方法)

Outline

Overview of Regularization

L0 regularization

L1 regularization

L2 regularization

Elastic Net regularization

L2,1 regularization

Model example

Reference

Overview of Regularization

Main goal:

1. Prevent over-fitting

2. Reduce prediction error

3. Improve generalization performance

Essence:

1. Constraints the parameters to be optimized

2. Minimize your error while regularizing your  parameters

L0 regularization

L1 regularization

L2 regularization

L1 vs. L2

Elastic Net regularization

L2,1 regularization

Reference

1. Sparsity and Some Basics of L1 Regularization

2. A note on the group lasso and a sparse group lasso

3. Hierarchical Structured Sparse Representation

4. 正态分布的前世今生

5. https://www.zhihu.com/question/20924039

6. Sparse methods for machine learning

Regularization method for machine learning的更多相关文章

  1. Classical method of machine learning

    PCA principal components analysis kmeans bayes spectral clustering svm EM hidden Markov models deep ...

  2. Machine Learning and Data Mining（机器学习与数据挖掘）

    Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcemen ...

  3. Machine Learning - 第3周（Logistic Regression、Regularization）

    Logistic regression is a method for classifying data into discrete outcomes. For example, we might u ...

  4. （原创）Stanford Machine Learning (by Andrew NG) --- (week 3) Logistic Regression & Regularization

    coursera上面Andrew NG的Machine learning课程地址为:https://www.coursera.org/course/ml 我曾经使用Logistic Regressio ...

  5. Andrew Ng Machine Learning 专题【Logistic Regression & Regularization】

    此文是斯坦福大学,机器学习界 superstar - Andrew Ng 所开设的 Coursera 课程:Machine Learning 的课程笔记. 力求简洁,仅代表本人观点,不足之处希望大家探 ...

  6. machine learning(14) --Regularization:Regularized linear regression

    machine learning(13) --Regularization:Regularized linear regression Gradient descent without regular ...

  7. Machine Learning Algorithms Study Notes(2)--Supervised Learning

    Machine Learning Algorithms Study Notes 高雪松 @雪松Cedro Microsoft MVP 本系列文章是Andrew Ng 在斯坦福的机器学习课程 CS 22 ...

  8. Machine Learning Algorithms Study Notes(1)--Introduction

    Machine Learning Algorithms Study Notes 高雪松 @雪松Cedro Microsoft MVP 目 录 1    Introduction    1 1.1    ...

  9. FAQ: Machine Learning: What and How

    What: 就是将统计学算法作为理论,计算机作为工具,解决问题.statistic Algorithm. How: 如何成为菜鸟一枚? http://www.quora.com/How-can-a-b ...

随机推荐

  1. mysql快速导入导出数据

    --导入 select * from inhos_genoperation(表名) where UPLOAD_ORG_CODE='***' into outfile '/tmp/inhos_genop ...

  2. C#实现文件上传以及文件下载

    public ActionResult Upload() { // var pathUrl = "http://" + Request.Url.Authority; var fil ...

  3. SPOJ8222 NSUBSTR - Substrings(后缀自动机)

    You are given a string S which consists of 250000 lowercase latin letters at most. We define F(x) as ...

  4. oracle约束约束状态和设计习惯

    oracle约束状态有几个项目,会让人迷惑,分别是: enable/disable--是否启用/禁用 validate/invalidate--确认/不确认 deferrable/not deferr ...

  5. 发布django项目

    supervisor需要用到的技术 1. nginx反向代理 2. nginx负载均衡 3. uwsgi 4. supervisor 5. virtualenv 安装nginx 详情参考 https: ...

  6. 一次 group by + order by 性能优化分析

    一次 group by + order by 性能优化分析 最近通过一个日志表做排行的时候发现特别卡,最后问题得到了解决,梳理一些索引和MySQL执行过程的经验,但是最后还是有5个谜题没解开,希望大家 ...

  7. jenkins+maven+docker集成java发布(一)自动发布

    JAVA项目持续集成发布 标签(空格分隔): java jenkins 微服务中持续集成自动发布是很重要的一个环节,将不同的模块应用自动部署到一台或者N台服务器中如果采用人工部署的方式不太现实 git ...

  8. ruby Dir类

    类方法 1. Dir[pat]    Dir::glob( pat) 返回一个数组,包含与指定的通配符模式 pat 匹配的文件名: * - 匹配包含 null 字符串的任意字符串 ** - 递归地匹配 ...

  9. 以源码安装的lamp环境为依托，源码安装zabbix监控系统

    1.源码安装lamp环境 1)安装httpd, 以源码httpd-2.4.33为基础,解压后,执行./configure --prefix=/usr/local/ --sysconfdir=/etc/ ...

  10. Leecode刷题之旅-C语言/python-83删除排序链表中的重复元素

    /* * @lc app=leetcode.cn id=83 lang=c * * [83] 删除排序链表中的重复元素 * * https://leetcode-cn.com/problems/rem ...