Evolutionary Machine Learning Techniques: Algorithms and Applications
暫譯: 進化式機器學習技術：演算法與應用
Mirjalili, Seyedali, Faris, Hossam, Aljarah, Ibrahim
- 出版商: Springer
- 出版日期: 2019-11-25
- 售價: $7,920
- 貴賓價: 9.5 折 $7,524
- 語言: 英文
- 頁數: 286
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9813299894
- ISBN-13: 9789813299894
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相關分類:
Machine Learning、Algorithms-data-structures
海外代購書籍(需單獨結帳)
商品描述
This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks.
The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
商品描述(中文翻譯)
這本書深入分析了當前進化機器學習技術。討論了最受推崇的分類、聚類、回歸和預測方法，包括支持向量機（support vector machines）、極限學習機（extreme learning machines）、進化特徵選擇（evolutionary feature selection）、人工神經網絡（artificial neural networks），包括前饋神經網絡（feed-forward neural networks）、多層感知器（multi-layer perceptron）、概率神經網絡（probabilistic neural networks）、自我優化神經網絡（self-optimizing neural networks）、徑向基函數網絡（radial basis function networks）、遞歸神經網絡（recurrent neural networks）、脈衝神經網絡（spiking neural networks）、神經模糊網絡（neuro-fuzzy networks）、模塊化神經網絡（modular neural networks）、物理神經網絡（physical neural networks）和深度神經網絡（deep neural networks）。
本書提供了機器學習的基本定義、文獻回顧以及使用經典和現代自然啟發技術的訓練算法。它還探討了經典訓練算法的優缺點。書中介紹了一系列經過驗證的、最近的自然啟發算法，用於訓練不同類型的人工神經網絡，包括遺傳算法（genetic algorithm）、蟻群優化（ant colony optimization）、粒子群優化（particle swarm optimization）、灰狼優化器（grey wolf optimizer）、鯨魚優化算法（whale optimization algorithm）、蟻獅優化器（ant lion optimizer）、蛾火焰算法（moth flame algorithm）、蜻蜓算法（dragonfly algorithm）、沙爾普群算法（salp swarm algorithm）、多宇宙優化器（multi-verse optimizer）和正弦餘弦算法（sine cosine algorithm）。本書還涵蓋了改進的人工神經網絡在解決各種領域的分類、聚類、預測和回歸問題中的應用。
作者簡介
Dr. Seyedali Mirjalili is a lecturer at Griffith College, Griffith University, and internationally recognised for his advances in nature-inspired artificial intelligence (AI) techniques. He is the author of five books, 100 journal articles, 20 conference papers, and 20 book chapters. With over 10000 citations and H-index of 40, he is one of the most influential AI researchers in the world. From Google Scholar metrics, he is globally the 3rd most cited researcher in Engineering Optimisation and Robust Optimisation using AI techniques. He has been the keynote speaker of several international conferences and is serving as an associate editor of top AI journals including Applied Soft Computing, Applied Intelligence, IEEE Access, Advances in Engineering Software, and Applied Intelligence.
Hossam Faris is a Professor in the Information Technology Department at King Abdullah II School for Information Technology at The University of Jordan, Jordan. Hossam Faris received his B.A. and M.Sc. degrees in computer science from the Yarmouk University and Al-Balqa` Applied University in 2004 and 2008, respectively, in Jordan. He was awarded a full-time competition-based scholarship from the Italian Ministry of Education and Research to peruse his Ph.D. degrees in e-Business at the University of Salento, Italy, where he obtained his Ph.D. degree in 2011. In 2016, he worked as a postdoctoral researcher with the GeNeura team at the Information and Communication Technologies Research Center (CITIC), University of Granada, Spain. His research interests include applied computational intelligence, evolutionary computation, knowledge systems, data mining, semantic web, and ontologies.
Dr. Aljarah is an Associate Professor of BIG Data Mining and Computational Intelligence at The University of Jordan--Department of Information Technology, Jordan. Currently, he is the Director Assistant to International Affairs Unit at The University of Jordan. He obtained the bachelor degree in computer science from the Yarmouk University, Jordan, 2003. He also obtained his master degree in computer science and information systems from the Jordan University of Science and Technology, Jordan, in 2006. He participated in many conferences in the fields of data mining, machine learning, and big data such as CEC, GECCO, NTIT, CSIT, IEEE NABIC, CASON, and BigData Congress. Furthermore, he contributed in many projects in USA such as Vehicle Class Detection System (VCDS), Pavement Analysis Via Vehicle Electronic Telemetry (PAVVET), and Farm Cloud Storage System (CSS) projects. He has published more than 35 papers in refereed international conferences and journals. His research focuses on data mining, machine learning, big data, MapReduce, Hadoop, swarm intelligence, evolutionary computation, social network analysis (SNA), and large-scale distributed algorithms.
作者簡介(中文翻譯)
Dr. Seyedali Mirjalili 是格里菲斯大學格里菲斯學院的講師，因其在自然啟發的人工智慧 (AI) 技術方面的進展而享有國際聲譽。他是五本書籍、100篇期刊文章、20篇會議論文和20章書籍的作者。擁有超過10000次引用和40的H指數，他是全球最具影響力的AI研究者之一。根據Google Scholar的指標，他在使用AI技術的工程優化和穩健優化領域中，全球排名第三的被引用研究者。他曾擔任多個國際會議的主題演講者，並擔任多本頂尖AI期刊的副編輯，包括《Applied Soft Computing》、《Applied Intelligence》、《IEEE Access》、《Advances in Engineering Software》和《Applied Intelligence》。
Hossam Faris 是約旦國王阿卜杜拉二世資訊科技學校資訊科技系的教授。Hossam Faris於2004年和2008年分別在約旦的雅爾穆克大學和阿爾巴爾卡應用大學獲得計算機科學的學士和碩士學位。他獲得了意大利教育和研究部的全職競爭獎學金，以在意大利薩倫托大學攻讀電子商務的博士學位，並於2011年獲得博士學位。2016年，他在西班牙格拉納達大學的資訊與通信技術研究中心 (CITIC) 與GeNeura團隊合作擔任博士後研究員。他的研究興趣包括應用計算智能、進化計算、知識系統、數據挖掘、語義網和本體論。
Dr. Aljarah 是約旦大學資訊科技系的BIG數據挖掘和計算智能副教授。目前，他是約旦大學國際事務單位的助理主任。他於2003年在約旦的雅爾穆克大學獲得計算機科學學士學位，並於2006年在約旦科學與技術大學獲得計算機科學和資訊系統的碩士學位。他參加了許多數據挖掘、機器學習和大數據領域的會議，如CEC、GECCO、NTIT、CSIT、IEEE NABIC、CASON和BigData Congress。此外，他還參與了美國的多個項目，如車輛類別檢測系統 (VCDS)、通過車輛電子遙測進行的路面分析 (PAVVET) 和農場雲存儲系統 (CSS) 項目。他在經過審核的國際會議和期刊上發表了超過35篇論文。他的研究重點包括數據挖掘、機器學習、大數據、MapReduce、Hadoop、群體智能、進化計算、社交網絡分析 (SNA) 和大規模分佈式算法。
