机器学习基础

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机器学习应用

最早的机器学习：垃圾邮件分辨，对于邮件如何判断邮件是否是垃圾邮件呢
传统的解决思路：编写规则，定义"垃圾邮件"，让计算机执行

1.编写一个传统算法，定义"垃圾邮件"，让计算机执行
2.对于很多问题，规则很难定义
3.规则在不断变化
图像识别：分辨图像表示的是什么，典型问题就是二分类的问题
机器学习的算法，如何评价算法的好坏
如何解决过拟合和欠拟合的情况
如何调节算法的参数
验证算法的正确性

机器学习基础

数据

萼片长度	萼片宽度	花瓣长度	花瓣宽度	种类
5.1	3.5	1.4	0.2	se(0)
7.0	3.2	4.7	1.4	ve(1)
6.3	3.3	6	2.5	vi(2)

以莺尾花数据集为例，介绍数据

• 数据整体叫数据集(data set)
• 每一行数据称为一个样本(sample)
• 除最后一列，每一列表达样本的一个特征(feature)
• 最后一列表示的是结果，通常称为标签(label)
  对于数据特征，我们用X表示
  通常第i个样本写作Xi第i个样本第j个特征值Xji​,第i个标记写作yi
  行是特征，每一行表示特征向量Xi
  特征空间(feature space)
  分类任务本身就是在特征空间切分
  在高位空间同理
  特征可以很抽象
• 图像，每一个像素点都是特征
• 28* 28的图像有784个特征
• 如果是彩色的特征更多

分类任务

一般来说分类就是二分类的任务
多分类任务
一些算法只能完成二分类任务
多分类任务可以转换成为二分类任务
有一些算法天然可以完成多分类任务
多标签分类
回归任务

回归任务：结果是一个连续的数字的值，而非一个类别
房屋价格
市场分析
学生成绩
股票价格

• 有一些算法只能解决回归问题
• 有一些算法只能解决分类问题

什么是机器学习

输入大量资料->机器学习算法->模型f(x)->输入样例->输出结果

监督学习、非监督学习、半监督学习和强化学习

• 监督学习：给机器的训练数据拥有"标记"或者"答案"
  图像已经拥有了标定信息 银行已经积累了一定的客户信息和信用卡的信用卡情况 医院积累一定的病人信息和最终确诊是否患病的情况 市场积累了房屋的基本信息和最终成交的金额

 

 

• 非监督学习：给机器的训练数据没有任何标记和答案
  非监督学习的意义：
  1.对没有进行标记的数据进行分类-聚类分析
  2.对数据进行降维处理

  • 特征提取：信用卡的信用评级和人的胖瘦关系
  • 特征压缩：PCA，针对高维数据进行处理，尽量少的损失，将高维数据压缩为低维数据
  • 非监督学习的降维处理意义：便于可视化
  • 异常检测：数据中存在的点具有异常的值

• 半监督学习：一部分数据有"标记"或"答案"，另一部分数据没有 (更常见)各种原因的缺失
  先使用无监督学习手段对数据进行处理，之后使用监督学习手段做模型的训练和预测

• 增强学习：根据周围环境，采取行动，根据采取行动的结果，学习行动方式

• 行动中加上奖励、惩罚机制，然后进行反馈循环

• 

机器学习的其他分类

• 批量学习(离线学习) Batch Learning
  优点：简单
  问题：如何适应环境的变化
  解决方案：定时重新批量学习
  缺点：每次重新批量学习，运算量巨大
• 在线学习 Online Learning

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