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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
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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