Metalearning: Applications to Automated Machine Learning and Data Mining
暫譯: 元學習：自動化機器學習與資料挖掘的應用

金屬學習（Metalearning）是研究利用元知識（metaknowledge）來獲得高效模型和解決方案的原則性方法，通過調整機器學習（machine learning）和資料探勘（data mining）過程來實現。儘管目前可用的各種機器學習和資料探勘技術在原則上可以提供良好的模型解決方案，但仍然需要一種方法論來有效地指導尋找最合適的模型。金屬學習提供了一種這樣的方法論，使系統能夠通過經驗變得更加有效。

本書討論了幾種獲取有關機器學習和資料探勘算法性能的知識的方法。它展示了如何重用這些知識來選擇、組合、組成和調整算法和模型，以便更快、更有效地解決資料探勘問題。因此，它可以幫助開發人員改進他們的算法，並開發能夠自我改進的學習系統。

本書將對機器學習、資料探勘和人工智慧領域的研究人員和研究生感興趣。

Pavel B. Brazdil is a senior researcher at LIAAD INESC TEC, Porto and Full Professor at FEP, University of Porto, Portugal and since 2019, Professor Emeritus. He obtained his PhD in machine learning in 1981 at the University of Edinburgh. Since the 1990s he has pioneered the area of metalearning and supervised various PhD students in this area. His main interests lie in machine learning, data mining, algorithm selection, metalearning, AutoML and text mining, among others. He has edited 6 books and more than 110 papers referenced on Google Scholar, of which approximately 80 are also on ISI/DBLP/Scopus. He was a program chair of various machine learning conferences (e.g., in 1992,2005), has co-organized various workshops on metalearning and acted as a co-editor of two special issues of MLJ on this topic. He is a member of the editorial board of the Machine Learning Journal and a Fellow of EurAI.

Jan N. van Rijn obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands). During his PhD, he made several funded research visits to the University of Waikato (New Zealand) and University of Porto (Portugal). After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). He currently holds a position as assistant professor at LIACS, Leiden University. His research aim is to democratize the access to machine learning and artificial intelligence across societal institutions. He is one of the founders of OpenML.org, an open science platform for machine learning. His research interests include artificial intelligence, automated machine learning and metalearning.

Carlos Soares is an Associate Professor at the Faculty of Engineering of U. Porto. Carlos is also an External Advisor for Intelligent Systems at Fraunhofer Portugal AICOS, a researcher at LIACC and a collaborator at LIAAD-INESC TEC. He is also a lecturer at the Porto Business School. The focus of his research is on metalearning/autoML but he has a general interest in Data Science. He has participated in 20+ national and international R&ID, as well as consulting projects. Carlos regularly collaborates with companies, including recent projects with Feedzai, Accenture and InovRetail. He has published/edited several books and 150+ papers in journals and conferences, (90+/125+ indexed by ISI/Scopus) and supervised 10+/50+ Ph.D./M.Sc. theses. Recent participation in the organization of events, includes ECML PKDD 2015, IDA 2016 and Discovery Science 2021 as programme co-chair. In 2009, he was awarded the Scientific Merit and Excellence Award of the Portuguese AI Association.

Joaquin Vanschoren is a tenured Assistant Professor of machine learning at the Eindhoven University of Technology (TU/e). He received his PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on the automation of machine learning (AutoML) and metalearning. He founded and leads OpenML.org, an online platform used all over the world for sharing machine learning data, algorithms, and models. He also chairs the Open Machine Learning Foundation, and co-chairs the W3C Machine Learning Schema Community Group. He is the recipient of an Amazon Research Award, an Azure Research Award, the Dutch Data Prize, and an ECMLPKDD demonstration award. He is a co-author and co-editor of the book "Automatic Machine Learning: Methods, Systems, Challenges". He has been tutorial speaker at NeurIPS, AAAI, and ECMLPKDD, and an invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NeurIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NeurIPS, and many other occasions. He was general chair at LION 2016, datasets and benchmarks chair at NeurIPS 2021, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and Meta-Learning workshop series at NeurIPS and ICML from 2013 to 2021.

Pavel B. Brazdil 是葡萄牙波爾圖大學 FEP 的全職教授及 LIAAD INESC TEC 的高級研究員，自 2019 年起擔任名譽教授。他於 1981 年在愛丁堡大學獲得機器學習博士學位。自 1990 年代以來，他在元學習（metalearning）領域開創了先河，並指導了多位該領域的博士生。他的主要研究興趣包括機器學習、資料探勘、演算法選擇、元學習、自動化機器學習（AutoML）和文本探勘等。他編輯了 6 本書籍，並在 Google Scholar 上發表了超過 110 篇論文，其中約 80 篇也被 ISI/DBLP/Scopus 收錄。他曾擔任多個機器學習會議的程式主席（例如，1992 年、2005 年），共同組織了多個元學習工作坊，並擔任《Machine Learning Journal》兩個特刊的共同編輯。他是《Machine Learning Journal》的編輯委員會成員，也是 EurAI 的研究員（Fellow）。

Jan N. van Rijn 於 2016 年在荷蘭萊頓大學的高級計算機科學研究所（LIACS）獲得計算機科學博士學位。在攻讀博士學位期間，他曾多次獲得資助前往新西蘭的懷卡托大學和葡萄牙的波爾圖大學進行研究訪問。獲得博士學位後，他在德國弗賴堡大學的機器學習實驗室擔任博士後研究員，該實驗室由 Frank Hutter 教授領導，之後他轉至美國紐約市的哥倫比亞大學擔任博士後研究員。目前，他在萊頓大學的 LIACS 擔任助理教授。他的研究目標是使社會機構能夠民主化地獲取機器學習和人工智慧。他是 OpenML.org 的創始人之一，這是一個用於機器學習的開放科學平台。他的研究興趣包括人工智慧、自動化機器學習和元學習。

Carlos Soares 是波爾圖大學工程學院的副教授。Carlos 同時擔任 Fraunhofer Portugal AICOS 的智能系統外部顧問、LIACC 的研究員以及 LIAAD-INESC TEC 的合作者。他也是波爾圖商學院的講師。他的研究重點是元學習/自動化機器學習（autoML），但他對資料科學也有廣泛的興趣。他參與了 20 多個國內外的研究與開發（R&D）以及諮詢項目。Carlos 定期與公司合作，包括最近與 Feedzai、Accenture 和 InovRetail 的項目。他已發表/編輯多本書籍和 150 多篇期刊及會議論文（90+/125+ 被 ISI/Scopus 收錄），並指導了 10+/50+ 篇博士/碩士論文。最近參與的活動組織包括 ECML PKDD 2015、IDA 2016 和 Discovery Science 2021 的程式共同主席。2009 年，他獲得了葡萄牙人工智慧協會的科學優異獎。

Joaquin Vanschoren 是埃因霍溫科技大學（TU/e）機器學習的終身助理教授。他在比利時的天主教魯汀大學獲得博士學位。他的研究專注於機器學習的自動化（AutoML）和元學習。他創立並領導 OpenML.org，這是一個全球使用的在線平台，用於共享機器學習數據、演算法和模型。他還擔任開放機器學習基金會的主席，並共同主持 W3C 機器學習架構社群小組。他曾獲得亞馬遜研究獎、Azure 研究獎、荷蘭數據獎和 ECMLPKDD 示範獎。他是《Automatic Machine Learning: Methods, Systems, Challenges》一書的共同作者和共同編輯。他曾在 NeurIPS、AAAI 和 ECMLPKDD 擔任教程演講者，並在 ECDA、StatComp、AutoML@ICML、CiML@NeurIPS、DEEM@SIGMOD、AutoML@PRICAI、MLOSS@NeurIPS 等多個場合擔任受邀演講者。他曾擔任 LION 2016 的總主席、NeurIPS 2021 的數據集和基準主席、Discovery Science 2018 的程式主席、ECMLPKDD 2013 的示範主席，並於 2013 至 2021 年共同組織 NeurIPS 和 ICML 的 AutoML 和元學習工作坊系列。