April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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第九章：無監督學習技術
affinity propagation
這個演算法使用投票系統
，
讓實例票選代表它們的類似實例
，
在演算法收斂時
，
讓各
個代表實例與它的支持者形成一個群聚
。affinity propagation
可偵測任何數量及大小的
群聚
。
可惜這種演算法的計算複雜度是
O(m
2
)，
所以不太適合大型資料組
。
譜分群（spectral clustering）
這種演算法會先做一個實例彼此間的相似度矩陣
，
並且用它來製作一個二維的子矩陣
（
也就是將它降維
），
接著在這個二維空間中使用另一種分群演算法
（Scikit-Learn
的
作品是使用
K-Means）。
譜分群可以找到複雜的群聚結構
，
也可以用來切圖
（
例如在社
群網路找出朋友群聚
）。
它無法妥善處理大量實例
，
也不擅長處理尺寸差異很大的群
聚
。
接著我們來探討高斯混合模型
，
它可以用來估計密度
、
分群
，
以及檢測異常
。
高斯混合
高斯混合模型
（GMM）
是一種機率模型
，
它假設實例都是一些參數未知的高斯分布混合
物產生的
。
單一高斯分布產生的所有實例通常會形成一個類似橢圓形的群聚
。
每一個群聚
都可能有不同的橢圓外形
、
大小
、
密度與方向
，
就像圖
9-11
那樣
。
當你看到一個實例時
，
你知道它是其中一個高斯分布產生的
，
但你不知道究竟是哪一個
，
也不知道這些分布的參
數
。
GMM
有許多變體
，
最簡單的變體被做成
GaussianMixture
類別
，
使用它時
，
你必須先知
道高斯分布的數量
k。
我們假設資料組
X
是用下面的機率程序產生的
：
•
這個程序幫每一個實例從
k
個群聚隨機選出一個群聚
。
選擇第
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.