spectrum
範圍
The essece of machine learning:編程
Metaphor(比喻
): Credit approval
Applicant information:app
kind | info |
---|---|
age | 23 years |
gender | male |
annual salary | $30,000 |
years in residence | 1 year |
years in job | 1 year |
current debt | $15,000 |
... | ... |
可認爲這是一個d-維向量
其元素依次是 salary, years in residence, years in job, current debt...
y在這裏僅表示 extend credit (1) & not to extend credit (-1)
It is a function from domain X,
X is a set of all input x (the set of vectors of d-dimention), it's a d-dimention Euclidean space(歐氏空間
)
y : a binary co-demainendeavors
盡力
f is unknown, but g is known, and we credit it
the value of g is supposed to approximates f
爲何須要hypothesis setdom
no downside for including a hypothesis set in the formalization, but there is an upsideide
no downside:函數
upside:idea
quadratic programming
二次編程???glorious
最好的,極好的pinpoint
精確查找spa
x1 salary, x2 years in residence, x3 years in job, x4 current debt ... xd ...
根據實際狀況,分別給不一樣的權重
視爲credit scorethreshold
臨界值3d
we start with random weights that will give a random linenotation
符號,記法
引入 x0 = 1 能夠化簡表達式code
經過這種方法,咱們儘量的使這些點被正確分類
只要是線性的,當迭代次數足夠多後,總能所有分類正確component
premise
前提underlying process
基本過程
vending
販賣
雖然沒辦法知道具體類別,可是能夠作出分類
想這種樣例少,爲給出肯定函數的,實際上根據不一樣規則是能夠有不一樣答案的