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
異常檢測（anomaly detection）
目的是學習
「
正常
」
的資料長怎樣
，
接著用它來偵測不正常的實例
，
例如在生產線上
有缺陷的項目
，
或是在時間序列中的新趨勢
。
密度估計（density estimation）
這種任務的目的是估計產生資料集的隨機程序的
機率密度函數
（PDF）。
密度估計通
常用於異常檢測
：
在密度很低的區域中的實例很有可能是異常的
。
它也很適合用來分
析資料與視覺化
。
可以來塊蛋糕了嗎
？
我們將從分群法看起
，
使用
K-Means
與
DBSCAN，
接著討論高斯混合
模型
，
看一下如何用它來做密度估計
、
分群與異常檢測
。
分群法
（clustering）
當你在爬山時
，
偶爾發現了一種從未看過的植物
，
你環顧四周
，
發現更多這種植物
，
它們
並非長得一模一樣
，
但非常相似
，
所以你知道它們可能屬於同一個物種
（
或至少是同一
屬
）。
你可能要問植物學家才能知道那是什麼物種
，
但你不需要專家就可以認出一群看起
來很像的物件
，
這種能力稱為
分群
（clustering），
這項工作就是找出相似的實例
，
並且將
它們分到相似實例的
群體
（clusters）
或群聚
。
分群與分類都會將各個實例分到一個群組
，
它們不同的地方在於
，
分群是無監督任務
。
考
慮圖
9-1：
左圖是
iris
資料組
（
見第
4
章
），
它用不同的記號來表示各個實例的品種
（
也就
是它的類別
）。
它是個有標籤的資料組
，Logistic
回歸
、SVM
或隨機森林分類器都很適合
處理它
。
右邊是同一個資料組
，
但沒有標籤
，
所以你無法使用分類演算法了
。
這正是使用
分群演算法的時機
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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.