Week1.1_监督学习和无监督学习

1.监督学习:我们来教计算机如何“学习”，有两个大的分支，一个是 regression，另一个是 classification。监督学习人为给定的“标准答案”。regression ：“标准答案”是连续的，根据标准答案可作出出曲线函数，就可以预估其它未知输出的数据。 比如说，对三个月销售量的估计。

classification：“标准答案”是离散的，可以根据现行函数分成N个类型（eg.一分为二），输入数据，就可以知道分在哪一个类中，比如对是否患有乳腺癌的的判断。

supervised (classes are known to all)：
K-NN
NP
Naive Bayes
Decision Trees
SVM

 

非监督学习就没有标准答案了，clustering algorithm是典型算法，没有给定的函数划分，本质是一个相似的类型的会聚集在一起。比如说，给你一堆数据，让你来分析这堆数据的结构。比如Google新闻，每天会搜集各大网站的大量的新闻，然后把它们全部聚类，就会自动分成几十个不同的标题。

 

无监督学习还有一个典型的例子就是鸡尾酒会问题（声音的分离），在这个酒会上有两种声音，被两个不同的麦克风在不同的地方接收到，而可以利用无监督学习来分离这两种不同的声音。注意到这里是无监督学习的原因是，事先并不知道这些声音中有哪些种类（这里的种类就是标签的意思）。而且本题的代码实现只要一行。

作者：赵杨
链接：https://www.zhihu.com/question/23194489/answer/75555668
来源：知乎
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

 

unsupervised (no class):
K-means
hierarchical clustering

 

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

Example: playing checkers.

• E = the experience of playing many games of checkers
• T = the task of playing checkers.
• P = the probability that the program will win the next game

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into "regression" and "classification" problems. In aregression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables intodiscrete categories.

Example:

Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories.

Unsupervised Learning

Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It’s not just about clustering. For example, associative memory is unsupervised learning.

Example:

Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.

Associative: Suppose a doctor over years of experience forms associations in his mind between patient characteristics and illnesses that they have. If a new patient shows up then based on this patient’s characteristics such as symptoms, family medical history, physical attributes, mental outlook, etc the doctor associates possible illness or illnesses based on what the doctor has seen before with similar patients. This is not the same as rule based reasoning as in expert systems. In this case we would like to estimate a mapping function from patient characteristics into illnesses.