图书简介
The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Key FeaturesComprehensive, one-stop-shop for the various aspects of the broad research area of computer learning and intelligenceAddresses the most cutting edge issues, such as explainable AI, machine learning, and deep learning
Introduction (Plamen Angelov); Volume 1: Explainable AI: Explainable AI and Computational Intelligence: Past and Present (Mojtaba Yeganejou and Scott Dick); Fundamentals of Fuzzy Set Theory (Fernando Gomide); Granular Computing (Andrzej Bargiela and Witold Pedrycz); Evolving Fuzzy and Neuro-Fuzzy Systems - Fundamentals, Stability, Explainability, Useability and Applications (Edwin Lughofer); Incremental Fuzzy Machine Learning for Online Classification of Emotions in Games from EEG Data Streams (Daniel Leite, Volnei Frigeri Jr and Rodrigo Medeiros); Causal Reasoning (Wenhao Zhang, Ramin Ramezani and Arash Naeim); Supervised Learning: Fuzzy Classifiers (Hamid Bouchachia); Kernel Models and Support Vector Machines (Denis Kolev, Mikhail Suvorov and Dmitry Kangin); Evolving Connectionist Systems for Adaptive Learning and Knowledge Discovery: From Neuro-fuzzy-, to Spiking-, Neurogenetic- and Quantum-Inspired: A Review of Principles and Applications (Nikola Kasabov); Supervised Learning using Spiking Neural Networks (Abeegithan Jeyasothy, Shirin Dora, Sundaram Suresh and Narasimhan Sundararajan); Fault Detection and Diagnosis based on LSTM Neural Network Applied to a Level Control Pilot Plant (Emerson Vilar de Oliveira, Yuri Thomas Nunes, Mailson Ribeiro Santos and Luiz Affonso Guedes); Conversational Agents: Theory and Applications (Mattias Wahde and Marco Virgolin); Volume 2: Deep Learning: Deep Learning and Its Adversarial Robustness: A Brief Introduction (Fu Wang, Wenjie Ruan, Chi Zhang and PeiPei Xu); Deep Learning for Graph-Structured Data (Luca Pasa, Nicolò Navarin and Alessandro Sperduti); A Critical Appraisal on Deep Neural Networks: Bridge the Gap from Deep Learning to Neuroscience via XAI (Anna-Sophia Bartle, Ziping Jiang, Debanjan Konar, Fozia Mehboob, Richard Jiang, Ahmed Bouridane and Somaya Almaadeed); Ensemble Learning (Yong Liu and Qiangfu Zhao); A Multi-Stream Deep Rule-based Ensemble System for Aerial Image Scene Classification (Xiaowei Gu and Plamen Angelov); Intelligent Control: Fuzzy Model-based Control: Predictive and Adaptive Approach (Igor Škrjanc and Sašo Blažič); Reinforcement Learning with Applications in Automation Control and Game Theory (Kyriakos G Vamvoudakis, Frank L Lewis, Draguna Vrabie); Nature-inspired Optimal Tuning of Fuzzy Controllers (Radu-Emil Precup and Radu-Codrut David); Indirect Self-evolving Fuzzy Control Approaches and Their Applications (Zhao-Xu Yang and Hai-Jun Rong); Evolutionary Computation: Evolutionary Computation: History View and Basic Concepts (Carlos A. Coello Coello, Carlos Segura and Gara Miranda); An Empirical Study of Algorithmic Bias (Dipankar Dasgupta and Sanjib Sen); Collective Intelligence: A Comprehensive Review of Methaheuristic Algorithms Inspired by Animals (Fevrier Valdez); Fuzzy Dynamic Parameter Adaptation for Grey Wolf Optimization of Modular Granular Neural Networks applied to Human Recognition using the iris biometric measure (Patricia Melin, Daniela Sanchez, and Oscar Castillo); Evaluating Inter-task Similarity for multifactorial Evolutionary Algorithm from Different Perspectives (Lei Zhou, Liang Feng, Min Jiang and Kay Chen Tan)
Trade Policy 买家须知
- 关于产品:
- ● 正版保障:本网站隶属于中国国际图书贸易集团公司,确保所有图书都是100%正版。
- ● 环保纸张:进口图书大多使用的都是环保轻型张,颜色偏黄,重量比较轻。
- ● 毛边版:即书翻页的地方,故意做成了参差不齐的样子,一般为精装版,更具收藏价值。
关于退换货:
- 由于预订产品的特殊性,采购订单正式发订后,买方不得无故取消全部或部分产品的订购。
- 由于进口图书的特殊性,发生以下情况的,请直接拒收货物,由快递返回:
- ● 外包装破损/发错货/少发货/图书外观破损/图书配件不全(例如:光盘等)
并请在工作日通过电话400-008-1110联系我们。
- 签收后,如发生以下情况,请在签收后的5个工作日内联系客服办理退换货:
- ● 缺页/错页/错印/脱线
关于发货时间:
- 一般情况下:
- ●【现货】 下单后48小时内由北京(库房)发出快递。
- ●【预订】【预售】下单后国外发货,到货时间预计5-8周左右,店铺默认中通快递,如需顺丰快递邮费到付。
- ● 需要开具发票的客户,发货时间可能在上述基础上再延后1-2个工作日(紧急发票需求,请联系010-68433105/3213);
- ● 如遇其他特殊原因,对发货时间有影响的,我们会第一时间在网站公告,敬请留意。
关于到货时间:
- 由于进口图书入境入库后,都是委托第三方快递发货,所以我们只能保证在规定时间内发出,但无法为您保证确切的到货时间。
- ● 主要城市一般2-4天
- ● 偏远地区一般4-7天
关于接听咨询电话的时间:
- 010-68433105/3213正常接听咨询电话的时间为:周一至周五上午8:30~下午5:00,周六、日及法定节假日休息,将无法接听来电,敬请谅解。
- 其它时间您也可以通过邮件联系我们:customer@readgo.cn,工作日会优先处理。
关于快递:
- ● 已付款订单:主要由中通、宅急送负责派送,订单进度查询请拨打010-68433105/3213。
本书暂无推荐
本书暂无推荐