Learning From Data Streams In Dynamic Environments

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Learning from Data Streams in Dynamic Environments

Author : Moamar Sayed-Mouchaweh
Publisher : Springer
ISBN 13 : 331925667X
Total Pages : 75 pages
Book Rating : 4.3/5 (192 download)

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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 75 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 : 317 pages
Book Rating : 4.3/5 (198 download)

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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 317 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 : 026254783X
Total Pages : 289 pages
Book Rating : 4.2/5 (625 download)

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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 2023-05-09 with total page 289 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 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)

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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)

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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

Learning from Data Streams

Author : João Gama
Publisher : Springer Science & Business Media
ISBN 13 : 3540736794
Total Pages : 244 pages
Book Rating : 4.5/5 (47 download)

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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 2007-09-20 with total page 244 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.

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)

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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.

Machine Learning: ECML-93

Author : Pavel B. Brazdil
Publisher : Springer Science & Business Media
ISBN 13 : 9783540566021
Total Pages : 492 pages
Book Rating : 4.5/5 (66 download)

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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 Science & Business Media. This book was released on 1993-03-23 with total page 492 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.

Machine Learning and Data Mining in Pattern Recognition

Author : Petra Perner
Publisher : Springer
ISBN 13 : 331941920X
Total Pages : 807 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Machine Learning and Data Mining in Pattern Recognition by : Petra Perner

Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer. This book was released on 2016-06-27 with total page 807 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Discovery Science

Author : João Gama
Publisher : Springer
ISBN 13 : 3642047475
Total Pages : 487 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Discovery Science by : João Gama

Download or read book Discovery Science written by João Gama and published by Springer. This book was released on 2009-10-07 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.

Anomaly Detection and Complex Event Processing Over IoT Data Streams

Author : Patrick Schneider
Publisher : Academic Press
ISBN 13 : 0128238194
Total Pages : 408 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Anomaly Detection and Complex Event Processing Over IoT Data Streams by : Patrick Schneider

Download or read book Anomaly Detection and Complex Event Processing Over IoT Data Streams written by Patrick Schneider and published by Academic Press. This book was released on 2022-01-07 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge Covers extraction (Anomaly Detection) Illustrates new, scalable and reliable processing techniques based on IoT stream technologies Offers applications to new, real-time anomaly detection scenarios in the health domain

Metalearning

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

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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 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)

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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.

Smart Embedded Systems

Author : Arun Sinha
Publisher : CRC Press
ISBN 13 : 1003810268
Total Pages : 300 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Smart Embedded Systems by : Arun Sinha

Download or read book Smart Embedded Systems written by Arun Sinha and published by CRC Press. This book was released on 2023-12-01 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Smart Embedded Systems: Advances and Applications" is a comprehensive guide that demystifies the complex world of embedded technology. The book journeys through a wide range of topics from healthcare to energy management, autonomous robotics, and wireless communication, showcasing the transformative potential of intelligent embedded systems in these fields. This concise volume introduces readers to innovative techniques and their practical applications, offers a comparative analysis of wireless protocols, and provides efficient resource allocation strategies in IoT-based ecosystems. With real-world examples and in-depth case studies, it serves as an invaluable resource for students and professionals seeking to harness the power of embedded technology to shape our digital future. Salient Features: The book provides a comprehensive coverage of various aspects of smart embedded systems, exploring their design, implementation, optimization, and a range of applications. This is further enhanced by in-depth discussions on hardware and software optimizations aimed at improving overall system performance. A detailed examination of machine learning techniques specifically tailored for data analysis and prediction within embedded systems. This complements the exploration of cutting-edge research on the use of AI to enhance wireless communications. Real-world applications of these technologies are extensively discussed, with a focus on areas such as seizure detection, noise reduction, health monitoring, diabetic care, autonomous vehicles, and communication systems. This includes a deep-dive into different wireless protocols utilized for data transfer in IoT systems. This book highlights key IoT technologies and their myriad applications, extending from environmental data collection to health monitoring. This is underscored by case studies on the integration of AI and IoT in healthcare, spanning topics from anomaly detection to informed clinical decision-making. Also featured is a detailed evaluation and comparison of different system implementations and methodologies This book is an essential read for anyone interested in the field of embedded systems. Whether you're a student looking to broaden your knowledge base, researchers looking in-depth insights, or professionals planning to use this cutting-edge technology in real-world applications, this book offers a thorough grounding in the subject.

Supervised and Unsupervised Learning for Data Science

Author : Michael W. Berry
Publisher : Springer Nature
ISBN 13 : 3030224759
Total Pages : 191 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Supervised and Unsupervised Learning for Data Science by : Michael W. Berry

Download or read book Supervised and Unsupervised Learning for Data Science written by Michael W. Berry and published by Springer Nature. This book was released on 2019-09-04 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

Soft Computing and Medical Bioinformatics

Author : Naresh Babu Muppalaneni
Publisher : Springer
ISBN 13 : 9811300593
Total Pages : 139 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Soft Computing and Medical Bioinformatics by : Naresh Babu Muppalaneni

Download or read book Soft Computing and Medical Bioinformatics written by Naresh Babu Muppalaneni and published by Springer. This book was released on 2018-06-13 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the applications of soft computing techniques in medical bioinformatics. It reflects the state-of-the-art research in soft computing and bioinformatics, including theory, algorithms, numerical simulations, and error and uncertainty analysis. It also deals with novel applications of new processing techniques in computer science. This book is useful to both students and researchers from computer science and engineering fields.

Computational Collective Intelligence

Author : Ngoc Thanh Nguyen
Publisher : Springer
ISBN 13 : 3319984438
Total Pages : 578 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Computational Collective Intelligence by : Ngoc Thanh Nguyen

Download or read book Computational Collective Intelligence written by Ngoc Thanh Nguyen and published by Springer. This book was released on 2018-08-27 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were carefully reviewed and selected from 240 submissions. The conference focuses on knowledge engineering and semantic web, social network analysis, recommendation methods and recommender systems, agents and multi-agent systems, text processing and information retrieval, data mining methods and applications, decision support and control systems, sensor networks and internet of things, as well as computer vision techniques.