• 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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    Win a contest, win a challenge
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  • 原文地址:https://www.cnblogs.com/pandaboy1123/p/10266113.html
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