Learning From Data Streams In Dynamic Environments

Download Learning From Data Streams In Dynamic Environments full books in PDF, epub, and Kindle. Read online Learning From Data Streams In Dynamic Environments ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Learning from Data Streams in Dynamic Environments

Author : Moamar Sayed-Mouchaweh
Publisher : Springer
ISBN 13 : 331925667X
Total Pages : 82 pages
Book Rating : 4.3/5 (192 download)

DOWNLOAD NOW!

Book Synopsis Learning from Data Streams in Dynamic Environments by : Moamar Sayed-Mouchaweh

Download or read book Learning from Data Streams in Dynamic Environments written by Moamar Sayed-Mouchaweh and published by Springer. This book was released on 2015-12-10 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.

Learning from Data Streams in Evolving Environments

Author : Moamar Sayed-Mouchaweh
Publisher : Springer
ISBN 13 : 3319898035
Total Pages : 320 pages
Book Rating : 4.3/5 (198 download)

DOWNLOAD NOW!

Book Synopsis Learning from Data Streams in Evolving Environments by : Moamar Sayed-Mouchaweh

Download or read book Learning from Data Streams in Evolving Environments written by Moamar Sayed-Mouchaweh and published by Springer. This book was released on 2018-07-28 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.

Machine Learning for Data Streams

Author : Albert Bifet
Publisher : MIT Press
ISBN 13 : 0262346052
Total Pages : 255 pages
Book Rating : 4.2/5 (623 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning for Data Streams by : Albert Bifet

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2018-03-16 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Learning in Non-Stationary Environments

Author : Moamar Sayed-Mouchaweh
Publisher : Springer Science & Business Media
ISBN 13 : 1441980202
Total Pages : 439 pages
Book Rating : 4.4/5 (419 download)

DOWNLOAD NOW!

Book Synopsis Learning in Non-Stationary Environments by : Moamar Sayed-Mouchaweh

Download or read book Learning in Non-Stationary Environments written by Moamar Sayed-Mouchaweh and published by Springer Science & Business Media. This book was released on 2012-04-13 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Learning from Data Streams

Author : João Gama
Publisher : Springer Science & Business Media
ISBN 13 : 3540736786
Total Pages : 486 pages
Book Rating : 4.5/5 (47 download)

DOWNLOAD NOW!

Book Synopsis Learning from Data Streams by : João Gama

Download or read book Learning from Data Streams written by João Gama and published by Springer Science & Business Media. This book was released on 2007-10-11 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.

Knowledge Discovery from Data Streams

Author : Joao Gama
Publisher : CRC Press
ISBN 13 : 1439826129
Total Pages : 256 pages
Book Rating : 4.4/5 (398 download)

DOWNLOAD NOW!

Book Synopsis Knowledge Discovery from Data Streams by : Joao Gama

Download or read book Knowledge Discovery from Data Streams written by Joao Gama and published by CRC Press. This book was released on 2010-05-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents

Machine Learning: ECML-93

Author : Pavel B. Brazdil
Publisher : Springer
ISBN 13 : 9783540475972
Total Pages : 480 pages
Book Rating : 4.4/5 (759 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning: ECML-93 by : Pavel B. Brazdil

Download or read book Machine Learning: ECML-93 written by Pavel B. Brazdil and published by Springer. This book was released on 2006-01-21 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops.

Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013

Author : Robert Burduk
Publisher : Springer Science & Business Media
ISBN 13 : 3319009699
Total Pages : 887 pages
Book Rating : 4.3/5 (19 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013 by : Robert Burduk

Download or read book Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013 written by Robert Burduk and published by Springer Science & Business Media. This book was released on 2013-05-23 with total page 887 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics Data Stream Classification and Big Data Analytics Features, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in robotics Speech and word recognition This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

Metalearning

Author : Pavel Brazdil
Publisher : Springer Science & Business Media
ISBN 13 : 3540732624
Total Pages : 182 pages
Book Rating : 4.5/5 (47 download)

DOWNLOAD NOW!

Book Synopsis Metalearning by : Pavel Brazdil

Download or read book Metalearning written by Pavel Brazdil and published by Springer Science & Business Media. This book was released on 2008-11-26 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications

Author : Vinit Kumar Gunjan
Publisher : Springer Nature
ISBN 13 : 9811664072
Total Pages : 821 pages
Book Rating : 4.8/5 (116 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications by : Vinit Kumar Gunjan

Download or read book Proceedings of the 2nd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications written by Vinit Kumar Gunjan and published by Springer Nature. This book was released on 2022-01-10 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains original, peer-reviewed research articles from the Second International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, held in March 28-29th 2021 at CMR Institute of Technology, Hyderabad, Telangana India. It covers the latest research trends and developments in areas of machine learning, artificial intelligence, neural networks, cyber-physical systems, cybernetics, with emphasis on applications in smart cities, Internet of Things, practical data science and cognition. The book focuses on the comprehensive tenets of artificial intelligence, machine learning and deep learning to emphasize its use in modelling, identification, optimization, prediction, forecasting and control of future intelligent systems. Submissions were solicited of unpublished material, and present in-depth fundamental research contributions from a methodological/application perspective in understanding artificial intelligence and machine learning approaches and their capabilities in solving a diverse range of problems in industries and its real-world applications.

Learning from Imbalanced Data Sets

Author : Alberto Fernández
Publisher : Springer
ISBN 13 : 3319980742
Total Pages : 385 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Learning from Imbalanced Data Sets by : Alberto Fernández

Download or read book Learning from Imbalanced Data Sets written by Alberto Fernández and published by Springer. This book was released on 2018-10-22 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Data Streams

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
ISBN 13 : 0387475346
Total Pages : 365 pages
Book Rating : 4.3/5 (874 download)

DOWNLOAD NOW!

Book Synopsis Data Streams by : Charu C. Aggarwal

Download or read book Data Streams written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2007-04-03 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.

Data Mining

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.:/5 (661 download)

DOWNLOAD NOW!

Book Synopsis Data Mining by : Fouad Sabry

Download or read book Data Mining written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-05 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Data Mining Data mining is the process of extracting and detecting patterns in huge data sets by utilizing approaches that lie at the confluence of machine learning, statistical analysis, and database management systems. Data mining is an interdisciplinary subject of computer science and statistics with the overarching goal of extracting information from a data set and translating the information into a structure that is understandable for the sake of subsequent application. The "knowledge discovery in databases" (also known as "KDD") method includes an analysis step that is known as "data mining." In addition to the phase of raw analysis, it also includes aspects of database management and data management, data pre-processing, model and inference considerations, interestingness measures, complexity considerations, post-processing of newly discovered structures, visualization, and online updating. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Data mining Chapter 2: Machine learning Chapter 3: Text mining Chapter 4: Association rule learning Chapter 5: Concept drift Chapter 6: Weka (software) Chapter 7: Special Interest Group on Knowledge Discovery and Data Mining Chapter 8: Educational data mining Chapter 9: Social media mining Chapter 10: Outline of machine learning (II) Answering the public top questions about data mining. (III) Real world examples for the usage of data mining in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of data mining' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of data mining.

Hybrid Artificial Intelligent Systems

Author : Pablo García Bringas
Publisher : Springer Nature
ISBN 13 : 3031407253
Total Pages : 789 pages
Book Rating : 4.0/5 (314 download)

DOWNLOAD NOW!

Book Synopsis Hybrid Artificial Intelligent Systems by : Pablo García Bringas

Download or read book Hybrid Artificial Intelligent Systems written by Pablo García Bringas and published by Springer Nature. This book was released on 2023-08-28 with total page 789 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2023, held in Salamanca, Spain, during September 5–7, 2023. The 65 full papers included in this book were carefully reviewed and selected from 120 submissions. They were organized in topical sections as follows: Anomaly and Fault Detection, Data Mining and Decision Support Systems, Deep Learning, Evolutionary Computation and Optimization, HAIS Applications, Image and Speech Signal Processing, Agents and Multiagents, Biomedical Applicatons.

Predictive Maintenance in Dynamic Systems

Author : Edwin Lughofer
Publisher : Springer
ISBN 13 : 3030056457
Total Pages : 564 pages
Book Rating : 4.0/5 (3 download)

DOWNLOAD NOW!

Book Synopsis Predictive Maintenance in Dynamic Systems by : Edwin Lughofer

Download or read book Predictive Maintenance in Dynamic Systems written by Edwin Lughofer and published by Springer. This book was released on 2019-02-28 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

AI and Blockchain Applications in Industrial Robotics

Author : Biradar, Rajashekhar C.
Publisher : IGI Global
ISBN 13 :
Total Pages : 432 pages
Book Rating : 4.3/5 (693 download)

DOWNLOAD NOW!

Book Synopsis AI and Blockchain Applications in Industrial Robotics by : Biradar, Rajashekhar C.

Download or read book AI and Blockchain Applications in Industrial Robotics written by Biradar, Rajashekhar C. and published by IGI Global. This book was released on 2023-12-29 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-evolving industrial landscape poses challenges for businesses, particularly in robotics, where performance optimization and data security are paramount. AI and Blockchain Applications in Industrial Robotics, edited by esteemed scholars Mihai Lazarescu, Rajashekhar Biradar, Geetha Devanagavi, Nikhath Tabassum, and Nayana Hegde, presents the transformative potential of combining AI and blockchain technologies to revolutionize the field. This exceptional book provides comprehensive insights into how AI enhances predictive models and pattern recognition, while blockchain ensures secure and immutable data transactions. By synergizing these technologies, businesses can achieve enhanced transparency, trust, and efficiency in their robotic processes. With practical applications, use cases, and real-world examples, the book caters to a wide range of readers, empowering them to embrace the possibilities of AI and blockchain in industrial robotics. AI and Blockchain Applications in Industrial Robotics equip industries with the tools and understanding to overcome challenges in optimizing performance, ensuring data security, and harnessing emerging technologies. Serving as a beacon of knowledge, this book drives innovation, efficiency, and competitiveness in the industrial sector. Whether for postgraduate students, researchers, industry professionals, undergraduate students, or freelance developers, the book provides valuable insights and practical guidance for implementing AI and blockchain solutions. By embracing the transformative potential of these technologies, industries can unlock new possibilities and propel themselves forward in the ever-advancing world of industrial robotics.

Big Data Analysis: New Algorithms for a New Society

Author : Nathalie Japkowicz
Publisher : Springer
ISBN 13 : 3319269895
Total Pages : 334 pages
Book Rating : 4.3/5 (192 download)

DOWNLOAD NOW!

Book Synopsis Big Data Analysis: New Algorithms for a New Society by : Nathalie Japkowicz

Download or read book Big Data Analysis: New Algorithms for a New Society written by Nathalie Japkowicz and published by Springer. This book was released on 2015-12-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.