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标题: 机器学习：实用案例解析 [打印本页]

作者: 清新de花 时间: 2015-11-21 11:34
标题: 机器学习：实用案例解析

机器学习：实用案例解析

目录
前言 ............................................................................... 1
第1章使用R语言 ............................................................ 9
R与机器学习 .........................................................................................................10
第2章数据分析 ............................................................ 36
分析与验证 ............................................................................................................36
什么是数据 ............................................................................................................37
推断数据的类型 ....................................................................................................40
推断数据的含义 ....................................................................................................42
数值摘要表 ............................................................................................................43
均值、中位数、众数 .............................................................................................44
分位数 ...................................................................................................................46
标准差和方差 ........................................................................................................47
可视化分析数据 ....................................................................................................49
列相关的可视化 ....................................................................................................68
第3章分类：垃圾过滤 .................................................. 77
非此即彼：二分类 .................................................................................................77
漫谈条件概率 ........................................................................................................81
试写第一个贝叶斯垃圾分类器 ..............................................................................82

第4章排序：智能收件箱 ............................................... 97
次序未知时该如何排序 .........................................................................................97
按优先级给邮件排序 .............................................................................................98
实现一个智能收件箱 ...........................................................................................102
第5章回归模型：预测网页访问量 ............................... 128
回归模型简介 ......................................................................................................128
预测网页流量 ......................................................................................................142
定义相关性 ..........................................................................................................152
第6章正则化：文本回归 ............................................. 155
数据列之间的非线性关系：超越直线 .................................................................155
避免过拟合的方法 ...............................................................................................164
文本回归 .............................................................................................................174
第7章优化：密码破译 ................................................ 182
优化简介 .............................................................................................................182
岭回归 .................................................................................................................188
密码破译优化问题 ...............................................................................................193
第8章 PCA：构建股票市场指数 .................................. 203
无监督学习 ..........................................................................................................203
主成分分析 ..........................................................................................................204
第9章 MDS：可视化地研究参议员相似性 .................... 212
基于相似性聚类 ..................................................................................................212
如何对美国参议员做聚类 ...................................................................................219
第10章 kNN：推荐系统 .............................................. 229
k近邻算法 ............................................................................................................229
R语言程序包安装数据 ........................................................................................235

第11章分析社交图谱 .................................................. 239
社交网络分析 ......................................................................................................239
用黑客的方法研究Twitter的社交关系图数据 ......................................................244
分析Twitter社交网络 ...........................................................................................252
第12章模型比较 ........................................................ 270
SVM：支持向量机 ..............................................................................................270
算法比较 .............................................................................................................280
参考文献 .................................................................... 287

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