自動機(jī)器學(xué)習(xí)：方法、系統(tǒng)與挑戰(zhàn)（AutoML）

定　價：￥89.00

作　者：	[德] 弗蘭克·亨特（Frank Hutter）著
出版社：	清華大學(xué)出版社
叢編項：
標(biāo)　簽：	暫缺

購買這本書可以去

京東 (￥75.70)

ISBN：	9787302552550	出版時間：	2020-10-01	包裝：	平裝
開本：	16開	頁數(shù)：	256	字?jǐn)?shù)：

內(nèi)容簡介

　　本書全面介紹自動機(jī)器學(xué)習(xí)，主要包含自動機(jī)器學(xué)習(xí)的方法、實際可用的自動機(jī)器學(xué)習(xí)系統(tǒng)及目前所面臨的挑戰(zhàn)。在自動機(jī)器學(xué)習(xí)方法中，本書涵蓋超參優(yōu)化、元學(xué)習(xí)、神經(jīng)網(wǎng)絡(luò)架構(gòu)搜索三個部分，每一部分都包括詳細(xì)的內(nèi)容介紹、原理解讀、具體運用方法和存在的問題等。此外，本書還具體介紹了現(xiàn)有的各種可用的AutoML系統(tǒng)，如Auto-sklearn、Auto-WEKA及Auto-Net等，并且本書最后一章詳細(xì)介紹了具有代表性的AutoML挑戰(zhàn)賽及挑戰(zhàn)賽結(jié)果背后所蘊含的理念，有助于從業(yè)者設(shè)計出自己的AutoML系統(tǒng)。本書英文版是國際上第一本介紹自動機(jī)器學(xué)習(xí)的英文書，內(nèi)容全面且翔實，尤為重要的是涵蓋了z新的AutoML領(lǐng)域進(jìn)展和難點。本書作者和譯者學(xué)術(shù)背景扎實，保證了本書的內(nèi)容質(zhì)量。對于初步研究者，本書可以作為其研究自動機(jī)器學(xué)習(xí)方法的背景知識和起點；對于工業(yè)界從業(yè)人員，本書全面介紹了AutoML系統(tǒng)及其實際應(yīng)用要點；對于已經(jīng)從事自動機(jī)器學(xué)習(xí)的研究者，本書可以提供一個AutoMLz新研究成果和進(jìn)展的概覽?？傮w來說，本書受眾較為廣泛，既可以作為入門書，也可以作為專業(yè)人士的參考書。

作者簡介

　　弗蘭克？亨特，德國弗萊堡大學(xué)教授，機(jī)器學(xué)習(xí)實驗室負(fù)責(zé)人。主要研究統(tǒng)計機(jī)器學(xué)習(xí)、知識表示、自動機(jī)器學(xué)習(xí)及其應(yīng)用，獲得第一屆（2015/2016）、第二屆（2018/2019）自動機(jī)器學(xué)習(xí)比賽的世界冠軍。拉斯？特霍夫，美國懷俄明大學(xué)助理教授。主要研究深度學(xué)習(xí)、自動機(jī)器學(xué)習(xí)，致力于構(gòu)建領(lǐng)先且健壯的機(jī)器學(xué)習(xí)系統(tǒng)，領(lǐng)導(dǎo)Auto-WEKA項目的開發(fā)和維護(hù)。華昆？萬赫仁，荷蘭埃因霍溫理工大學(xué)助理教授。主要研究機(jī)器學(xué)習(xí)的逐步自動化，創(chuàng)建了共享數(shù)據(jù)開源平臺OpenML.org，并獲得微軟Azure研究獎和亞馬遜研究獎。譯者簡介何明，中國科學(xué)技術(shù)大學(xué)博士，目前為上海交通大學(xué)電子科學(xué)與技術(shù)方向博士后研究人員、好未來教育集團(tuán)數(shù)據(jù)中臺人工智能算法研究員。劉淇，中國科學(xué)技術(shù)大學(xué)計算機(jī)學(xué)院特任教授，博士生導(dǎo)師，中國計算機(jī)學(xué)會大數(shù)據(jù)專家委員會委員，中國人工智能學(xué)會機(jī)器學(xué)習(xí)專業(yè)委員會委員。

圖書目錄

目錄

自動機(jī)器學(xué)習(xí)方法

第1章超參優(yōu)化 ··································2

1.1 引言 ··············································2

1.2 問題定義 ·······································4

1.2.1 優(yōu)化替代方案：集成與邊緣化 ·············5

1.2.2 多目標(biāo)優(yōu)化 ···········································5

1.3 黑盒超參優(yōu)化 ·······························6

1.3.1 免模型的黑盒優(yōu)化方法 ························6

1.3.2 貝葉斯優(yōu)化 ···········································8

1.4 多保真度優(yōu)化 ······························13

1.4.1 基于學(xué)習(xí)曲線預(yù)測的早停法 ··············14

1.4.2 基于Bandit的選擇方法 ·····················15

1.4.3 保真度的適應(yīng)性選擇 ··························17

1.5 AutoML的相關(guān)應(yīng)用 ····················18

1.6 探討與展望 ··································20

1.6.1 基準(zhǔn)測試和基線模型 ··························21

1.6.2 基于梯度的優(yōu)化 ··································22

1.6.3 可擴(kuò)展性 ·············································22

1.6.4 過擬合和泛化性 ··································23

1.6.5 任意尺度的管道構(gòu)建 ··························24

參考文獻(xiàn)···············································25

第2章元學(xué)習(xí) ···································36

2.1 引言 ·············································36

2.2 模型評估中學(xué)習(xí) ··························37

2.2.1 獨立于任務(wù)的推薦 ······························38

2.2.2 配置空間的設(shè)計 ··································39

2.2.3 配置遷移 ·············································39

2.2.4 學(xué)習(xí)曲線 ·············································42

2.3 任務(wù)特性中學(xué)習(xí) ··························43

2.3.1 元特征 ·················································43

2.3.2 元特征的學(xué)習(xí) ·····································44

2.3.3 基于相似任務(wù)熱啟動優(yōu)化過程 ···········46

2.3.4 元模型 ·················································48

2.3.5 管道合成 ·············································49

2.3.6 調(diào)優(yōu)與否 ·············································50

2.4 先前模型中學(xué)習(xí) ··························50

第一篇

XVI

2.4.1 遷移學(xué)習(xí) ·············································51

2.4.2 針對神經(jīng)網(wǎng)絡(luò)的元學(xué)習(xí) ······················51

2.4.3 小樣本學(xué)習(xí) ·········································52

2.4.4 不止于監(jiān)督學(xué)習(xí) ··································54

2.5 總結(jié) ·············································55

參考文獻(xiàn)···············································56

第3章神經(jīng)網(wǎng)絡(luò)架構(gòu)搜索 ··················68

3.1 引言 ·············································68

3.2 搜索空間 ······································69

3.3 搜索策略 ······································73

3.4 性能評估策略 ······························76

3.5 未來方向 ······································78

參考文獻(xiàn)···············································80

自動機(jī)器學(xué)習(xí)系統(tǒng)

第4章 Auto-WEKA ···························86

4.1 引言 ·············································86

4.2 準(zhǔn)備工作 ······································88

4.2.1 模型選擇 ·············································88

4.2.2 超參優(yōu)化 ·············································88

4.3 算法選擇與超參優(yōu)化結(jié)合
（CASH） ···································89

4.4 Auto-WEKA ·································91

4.5 實驗評估 ······································93

4.5.1 對比方法 ·············································94

4.5.2 交叉驗證性能 ·····································96

4.5.3 測試性能 ·············································96

4.6 總結(jié) ·············································98

參考文獻(xiàn)···············································98

第5章 Hyperopt-sklearn ·················101

5.1 引言 ···········································101

5.2 Hyperopt背景 ····························102

5.3 Scikit-Learn模型選擇 ···············103

5.4 使用示例 ····································105

5.5 實驗 ···········································109

5.6 討論與展望 ································111

5.7 總結(jié) ···········································114

參考文獻(xiàn)·············································114

第6章 Auto-sklearn ························116

6.1 引言 ···········································116

6.2 CASH問題 ································118

6.3 改進(jìn) ···········································119

6.3.1 元學(xué)習(xí)步驟 ········································119

6.3.2 集成的自動構(gòu)建 ································121

6.4 Auto-sklearn系統(tǒng) ······················121

6.5 Auto-sklearn的對比試驗 ···········125

6.6 Auto-sklearn改進(jìn)項的評估 ·······127

6.7 Auto-sklearn組件的詳細(xì)分析 ···129

6.8 討論與總結(jié) ································134

6.8.1 討論 ···················································134

第二篇

XVII

6.8.2 使用示例 ···········································134

6.8.3 Auto-sklearn的擴(kuò)展 ··························135

6.8.4 總結(jié)與展望 ·······································136

參考文獻(xiàn)·············································136

第7章 Auto-Net ······························140

7.1 引言 ···········································140

7.2 Auto-Net 1.0 ·······························142

7.3 Auto-Net 2.0 ·······························144

7.4 實驗 ···········································151

7.4.1 基線評估 ···········································151

7.4.2 AutoML競賽上的表現(xiàn) ·····················152

7.4.3 Auto-Net 1.0與Auto-Net 2.0的對比····154

7.5 總結(jié) ···········································155

參考文獻(xiàn)·············································156

第8章 TPOT ··································160

8.1 引言 ···········································160

8.2 方法 ···········································161

8.2.1 機(jī)器學(xué)習(xí)管道算子 ····························161

8.2.2 構(gòu)建基于樹的管道 ····························162

8.2.3 優(yōu)化基于樹的管道 ····························163

8.2.4 基準(zhǔn)測試數(shù)據(jù) ···································163

8.3 實驗結(jié)果 ····································164

8.4 總結(jié)與展望 ································167

參考文獻(xiàn)·············································168

第9章自動統(tǒng)計 ······························170

9.1 引言 ···········································170

9.2 自動統(tǒng)計項目的基本結(jié)構(gòu) ·········172

9.3 應(yīng)用于時序數(shù)據(jù)的自動統(tǒng)計 ·····173

9.3.1 核函數(shù)上的語法 ································173

9.3.2 搜索和評估過程 ································175

9.3.3 生成自然語言性的描述 ····················175

9.3.4 與人類比較 ·······································177

9.4 其他自動統(tǒng)計系統(tǒng) ····················178

9.4.1 核心組件 ···········································178

9.4.2 設(shè)計挑戰(zhàn) ···········································179

9.5 總結(jié) ···········································180

參考文獻(xiàn)·············································180

自動機(jī)器學(xué)習(xí)挑戰(zhàn)賽

第10章自動機(jī)器學(xué)習(xí)挑戰(zhàn)賽分析 ···186

10.1 引言··········································187

10.2 問題形式化和概述 ···················190

10.2.1 問題的范圍 ·····································190

10.2.2 全模型選擇 ·····································191

10.2.3 超參優(yōu)化 ·········································192

10.2.4 模型搜索策略 ·································193

10.3 數(shù)據(jù)··········································197

10.4 挑戰(zhàn)賽協(xié)議 ······························201

10.4.1 時間預(yù)算和計算資源 ······················201

10.4.2 評分標(biāo)準(zhǔn) ·········································202

10.4.3 挑戰(zhàn)賽2015/2016中的輪次和階段 ····205

第三篇

10.4.4 挑戰(zhàn)賽2018中的階段 ····················206

10.5 結(jié)果··········································207

10.5.1 挑戰(zhàn)賽2015/2016上的得分 ···········207

10.5.2 挑戰(zhàn)賽2018上的得分 ····················209

10.5.3 數(shù)據(jù)集/任務(wù)的難度 ·······················210

10.5.4 超參優(yōu)化 ·········································217

10.5.5 元學(xué)習(xí) ·············································217

10.5.6 挑戰(zhàn)賽中使用的方法 ······················219

10.6 討論··········································224

10.7 總結(jié)··········································226

參考文獻(xiàn)·············································229