{"id":15643257,"url":"https://github.com/ysh329/statistical-learning-methods-note","last_synced_at":"2025-07-15T11:09:48.098Z","repository":{"id":83527298,"uuid":"71029192","full_name":"ysh329/statistical-learning-methods-note","owner":"ysh329","description":"李航《统计学习方法》笔记和 Python 实现（不基于任何代数运算库）。","archived":false,"fork":false,"pushed_at":"2018-11-04T09:00:41.000Z","size":304,"stargazers_count":58,"open_issues_count":0,"forks_count":21,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-30T10:11:55.246Z","etag":null,"topics":["machine-learning","notes","python","statistical-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ysh329.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-10-16T04:04:26.000Z","updated_at":"2025-03-16T12:28:38.000Z","dependencies_parsed_at":null,"dependency_job_id":"23142f9c-dbc8-4480-b876-09d2ac4f3027","html_url":"https://github.com/ysh329/statistical-learning-methods-note","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ysh329/statistical-learning-methods-note","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Fstatistical-learning-methods-note","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Fstatistical-learning-methods-note/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Fstatistical-learning-methods-note/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Fstatistical-learning-methods-note/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ysh329","download_url":"https://codeload.github.com/ysh329/statistical-learning-methods-note/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ysh329%2Fstatistical-learning-methods-note/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265430452,"owners_count":23764003,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","notes","python","statistical-learning"],"created_at":"2024-10-03T11:59:42.300Z","updated_at":"2025-07-15T11:09:48.076Z","avatar_url":"https://github.com/ysh329.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 《统计学习方法》笔记与算法实现\n\n## 说明\n李航《统计学习方法》笔记与算法的 Python 实现。测试样例是例题数据，学习笔记主要在[本人博客](http://yuenshome.cn)的[《统计学习方法》](http://yuenshome.cn/?cat=202)目录下，也可点击下方目录的章节名阅读笔记。笔记大部分为原书的内容摘抄。\n\n算法主要基于 Python 语言进行实现，为了原汁原味地用代码将算法描述，所有没有使用第三方的线性代数运算库（如 Numpy 或 Pandas 等）。代码注释尽可能完善完整，力求描述准确到位。如有错误，还望指出（[→ 点击这里提问题吧 ←](https://github.com/ysh329/statistical-learning-methods-note/issues)），不胜感激！\n\n## 目录\n\n* 第 1 章 统计学习方法概论\n* 第 2 章 [感知机](./chapter_2_perceptron/) [\\[感知机代码-原始形式\\]](./chapter_2_perceptron/Perceptron.py) [\\[感知机代码-对偶形式\\]](./chapter_2_perceptron/Dual-form_Perceptron.py)  \n* 第 3 章 [k近邻算法](./chapter_3_kNN/) [\\[k近邻代码\\]](./chapter_3_kNN/kNN.py) [\\[kd树简化版代码\\]](./chapter_3_kNN/Simple-kd-Tree.py) [\\[kd树完整版代码\\]](./chapter_3_kNN/kd-Tree.py) [\\[错误kd树代码\\]](./chapter_3_kNN/WrongKDTreeCodeDemo.py)\n* 第 4 章 [朴素贝叶斯法](./chapter_4_NaiveBayes/)\n* 第 5 章 决策树\n* 第 6 章 逻辑斯谛回归与最大熵模型\n* 第 7 章 支持向量机\n* 第 8 章 [提升方法](./chapter_8_boosting/) [\\[AdaBoost代码\\]](./chapter_8_boosting/AdaBoost.py)\n* 第 9 章 EM算法及其推广\n* 第 10 章 隐马尔科夫模型\n* 第 11 章 条件随机场\n* 第 12 章 统计学习方法总结\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fysh329%2Fstatistical-learning-methods-note","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fysh329%2Fstatistical-learning-methods-note","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fysh329%2Fstatistical-learning-methods-note/lists"}