Special Issue Adaptive And Online Learning In Non Stationary Environments
DOWNLOAD
Download Special Issue Adaptive And Online Learning In Non Stationary Environments PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Special Issue Adaptive And Online Learning In Non Stationary Environments book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Learning In Non Stationary Environments
DOWNLOAD
Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-04-13
Learning In Non Stationary Environments written by Moamar Sayed-Mouchaweh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-13 with Technology & Engineering categories.
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Adaptive And Intelligent Systems
DOWNLOAD
Author : Abdelhamid Bouchachia
language : en
Publisher: Springer
Release Date : 2014-08-13
Adaptive And Intelligent Systems written by Abdelhamid Bouchachia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-13 with Computers categories.
This book constitutes the proceedings of the International Conference on Adaptive and Intelligent Systems, ICAIS 2014, held in Bournemouth, UK, in September 2014. The 19 full papers included in these proceedings together with the abstracts of 4 invited talks, were carefully reviewed and selected from 32 submissions. The contributions are organized under the following topical sections: advances in feature selection; clustering and classification; adaptive optimization; advances in time series analysis.
Special Issue Adaptive And Online Learning In Non Stationary Environments
DOWNLOAD
Author : Edwin Lughofer
language : en
Publisher:
Release Date : 2015
Special Issue Adaptive And Online Learning In Non Stationary Environments written by Edwin Lughofer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Learning Automata
DOWNLOAD
Author : Kumpati S. Narendra
language : en
Publisher: Courier Corporation
Release Date : 2012-12-19
Learning Automata written by Kumpati S. Narendra and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-19 with Technology & Engineering categories.
This self-contained introductorytext on the behavior of learningautomata focuses on howa sequential decision-makerwith a finite number of choiceswould respond in a random environment. A must for all studentsof stochastic algorithms, this treatment is the workof two well-known scientists, one of whom provides a newIntroduction.Reprint of the Prentice-Hall, Inc, Englewood Cliffs, NewJersey, 1989 edition.
Proceeding Of International Conference On Computational Science And Applications
DOWNLOAD
Author : Subhash Bhalla
language : en
Publisher: Springer Nature
Release Date : 2020-01-04
Proceeding Of International Conference On Computational Science And Applications written by Subhash Bhalla and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-04 with Technology & Engineering categories.
The book consists of high-quality papers presented at the International Conference on Computational Science and Applications (ICCSA 2019), held at Maharashtra Institute of Technology World Peace University, Pune, India, from 7 to 9 August 2019. It covers the latest innovations and developments in information and communication technology, discussing topics such as soft computing and intelligent systems, web of sensor networks, drone operating systems, web of sensor networks, wearable smart sensors, automated guided vehicles and many more.
Transactions On Computational Science Viii
DOWNLOAD
Author : C. J. Kenneth Tan
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-10-19
Transactions On Computational Science Viii written by C. J. Kenneth Tan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-19 with Computers categories.
The 8th issue of the Transactions on Computational Science has been divided into two parts. Part I, prepared by Guest Editors Nadia Nedjah, Abdelhamid Bouchachia, and Luiza de Macedo Mourelle, consists of 5 detailed papers, presenting state-of-the-art research results on adaptive models for evolutionary computation and their application in various dynamic environments. The 6 papers in Part II take an in-depth look at selected computational science research in the areas of geometric computing, Euclidean distance transform, distributed systems, segmentation, visualization of monotone data, and data interpolation.
Ecai 2020
DOWNLOAD
Author : G. De Giacomo
language : en
Publisher: IOS Press
Release Date : 2020-09-11
Ecai 2020 written by G. De Giacomo and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-11 with Computers categories.
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.
Advanced Signal Processing Handbook
DOWNLOAD
Author : Stergios Stergiopoulos
language : en
Publisher: CRC Press
Release Date : 2017-09-08
Advanced Signal Processing Handbook written by Stergios Stergiopoulos and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-08 with Technology & Engineering categories.
Advances in digital signal processing algorithms and computer technology have combined to produce real-time systems with capabilities far beyond those of just few years ago. Nonlinear, adaptive methods for signal processing have emerged to provide better array gain performance, however, they lack the robustness of conventional algorithms. The challenge remains to develop a concept that exploits the advantages of both-a scheme that integrates these methods in practical, real-time systems. The Advanced Signal Processing Handbook helps you meet that challenge. Beyond offering an outstanding introduction to the principles and applications of advanced signal processing, it develops a generic processing structure that takes advantage of the similarities that exist among radar, sonar, and medical imaging systems and integrates conventional and nonlinear processing schemes.
Foundations Of Computational Intelligence Volume 2
DOWNLOAD
Author : Aboul-Ella Hassanien
language : en
Publisher: Springer
Release Date : 2009-05-27
Foundations Of Computational Intelligence Volume 2 written by Aboul-Ella Hassanien and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-27 with Technology & Engineering categories.
Foundations of Computational Intelligence Volume 2: Approximation Reasoning: Theoretical Foundations and Applications Human reasoning usually is very approximate and involves various types of - certainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on t- ory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for - proximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations Part-II: Approximate Reasoning – Success Stories and Real World Applications Part I on Approximate Reasoning – Theoretical Foundations contains four ch- ters that describe several approaches of fuzzy and Para consistent annotated logic approximation reasoning. In Chapter 1, “Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox” by Peters considers how a user might utilize fuzzy sets, near sets, and rough sets, taken separately or taken together in hybridizations as part of a computational intelligence toolbox. In multi-criteria decision making, it is necessary to aggregate (combine) utility values corresponding to several criteria (parameters).
Reinforcement Learning
DOWNLOAD
Author : Richard S. Sutton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Reinforcement Learning written by Richard S. Sutton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.