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Introduction ----- Stanford Machine Learning(by Andrew NG)Course Notes

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  最近学习了coursera上面Andrew NG的Machine learning课程,课程地址为:https://www.coursera.org/course/ml

  在Introduction部分NG较为系统的概括了Machine learning的一些基本概念,也让我接触了一些新的名词,这些名词在后续课程中会频繁出现:

Machine Learning Supervised Learning Unsupervised Learning Regression Problem Classification Problem Octave
机器学习 有监督学习 无监督学习 回归问题 分类问题 Octave

What is Machine Learning

  Definition: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

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Example of Machine Learning

Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam.

T: Classifying emails as spam or not spam; (目标)

E: Watching you label emails as spam or not spam; (算法 + 数据)

P: The number (or fraction) of emails correctly classified as spam/not spam. (评价方法->损失函数)


Supervised Learning

Definition: The goal is, given a labeled training data, to learn a function h so that h(x) is a “good” predictor for the corresponding value of y. A pair (x, y) is called a training example, x denoting “input” variables, also called features, and y denoting “output” or target variable that we are trying to predict.

When the target variable that we are trying to predict is continuous, we call the learning problem a regression problem. When the target can take on only a small number of discrete values, the learning problem is called a classification problem.

A.Example of Regression Problem

  Suppose we have a dataset giving the living areas and prices of 11 houses from Portland, Oregon:

Living area (feet2) Price (1000$s)
450 100
600 140
620 210
... ...

  We can plot this data:

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  So regression problem is to find a function h to fit these points.

B.Example of Classification Problem

 

  Suppose we have a dataset giving the tumor size, patient age and malignant or benign, we plot these data as follows:

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So classification problem is to find a function h to sperate these points.

PS: 回归就是找出那个可以拟合样本的函数(平面,空间,...),分类就是找到那个可以把不同类别的样本分开的函数(平面,空间,...);在特定问题下,比如逻辑回归问题,分类问题就可以被视作回归问题来解决。


Unsupervised Learning

  In the clustering problem, we are given a training set {x(1), . . . , x(m)}, and want to group the data into a few cohesive “clusters”. Here, no labels y(i) are given. So, this is an unsupervised learning problem.

  PS: 无监督学习很多时候都暗指聚类算法,聚类算法又分硬聚类(K-means, 分层聚类,基于密度的等等)和软聚类(EM算法)。

 

 

 

Introduction ----- Stanford Machine Learning(by Andrew NG)Course Notes,码迷,mamicode.com

Introduction ----- Stanford Machine Learning(by Andrew NG)Course Notes

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原文地址:http://www.cnblogs.com/tec-vegetables/p/3696041.html

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