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

  • 机器学习基础_第1张图片

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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