April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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習題
|
227
Linear Discriminant Analysis (LDA)
它是分類演算法
，
但是在訓練期間
，
它會學習最能夠分辨不同類別的軸
，
接著用這些
軸來定義超平面
，
在上面投射資料
。
這種做法的好處在於
，
投射的結果可讓類別盡可
能地分開
，
所以
LDA
是在執行其他的分類演算法
（
例如
SVM
分類器
）
之前
，
很好的
降維技術
。
圖
8-13
是這些技術產生的結果
。
圖
8-13
使用各種技術來將瑞士捲降成
2D
習題
1. 降低資料組維數的主要動機是什麼？主要的缺點是什麼？
2. 什麼是維數災難？
3. 資料組被降維之後可以還原嗎？如果可以，怎麼做？如果不行，為什麼？
4. PCA 可用來降低高度非線性的資料組的維數嗎？
5. 假如你對著一個 1,000 維的資料組執行 PCA，將已解釋變異度比率設為 95%。產生
的資料組有幾維？
6. 你會在什麼情況下使用一般 PCA、Incremental PCA、Randomized PCA 或 Kernel
PCA ？
7. 如何評估降維演算法處理資料組的效果？
8. 將兩個不同的降維演算法接起來有意義嗎？
228
|
第八章：降維
9. 載入 MNIST 資料組（見第 3 章），並將它拆成訓練組與測試組（用前 60,000 個實例
來訓練，其餘的用 10,000 ...
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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.