Machine Learning On Data Streams
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Machine Learning For Data Streams
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Author : Albert Bifet
language : en
Publisher: MIT Press
Release Date : 2018-03-16
Machine Learning For Data Streams written by Albert Bifet and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-16 with Computers categories.
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.
Machine Learning On Data Streams
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Author : Matthias Carnein
language : en
Publisher:
Release Date : 2019
Machine Learning On Data Streams written by Matthias Carnein and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Learning From Data Streams
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Author : João Gama
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-09-20
Learning From Data Streams written by João Gama 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 2007-09-20 with Computers categories.
Sensor networks consist of distributed autonomous devices that cooperatively monitor an environment. Sensors are equipped with capacities to store information in memory, process this information and communicate with their neighbors. Processing data streams generated from wireless sensor networks has raised new research challenges over the last few years due to the huge numbers of data streams to be managed continuously and at a very high rate. The 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. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education. This research monograph delivers to researchers and graduate students the state of the art in data stream processing in sensor networks. The huge bibliography offers an excellent starting point for further reading and future research.
Transactional Machine Learning With Data Streams And Automl
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Author : Sebastian Maurice
language : en
Publisher:
Release Date : 2021
Transactional Machine Learning With Data Streams And Automl written by Sebastian Maurice and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are frictionless (require minimal to no human intervention) and elastic (machine learning solutions that can scale up or down by controlling the number of data streams, algorithms, and users of the insights). This book will strengthen your knowledge of the inner workings of TML solutions using data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML introduces the industry challenges with applying machine learning to data streams. You will learn the framework that will help you in choosing business problems that are best suited for TML. You will also see how to measure the business value of TML solutions. You will then learn the technical components of TML solutions, including the reference and technical architecture of a TML solution. This book also presents a TML solution template that will make it easy for you to quickly start building your own TML solutions. Specifically, you are given access to a TML Python library and integration technologies for download. You will also learn how TML will evolve in the future, and the growing need by organizations for deeper insights from data streams. By the end of the book, you will have a solid understanding of TML. You will know how to build TML solutions with all the necessary details, and all the resources at your fingertips. You will: Discover transactional machine learning Measure the business value of TML Choose TML use cases Design technical architecture of TML solutions with Apache Kafka Work with the technologies used to build TML solutions Build transactional machine learning solutions with hands-on code together with Apache Kafka in the cloud.
Knowledge Discovery From Data Streams
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Author : Joao Gama
language : en
Publisher: CRC Press
Release Date : 2010-05-25
Knowledge Discovery From Data Streams written by Joao Gama and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-25 with Business & Economics categories.
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 a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.
Learning From Data Streams In Evolving Environments
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Author : Moamar Sayed-Mouchaweh
language : en
Publisher: Springer
Release Date : 2018-07-28
Learning From Data Streams In Evolving Environments written by Moamar Sayed-Mouchaweh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-28 with Technology & Engineering categories.
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.
Visualizing Streaming Data
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Author : Anthony Aragues
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-06-01
Visualizing Streaming Data written by Anthony Aragues and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-01 with Computers categories.
While tools for analyzing streaming and real-time data are gaining adoption, the ability to visualize these data types has yet to catch up. Dashboards are good at conveying daily or weekly data trends at a glance, though capturing snapshots when data is transforming from moment to moment is more difficult—but not impossible. With this practical guide, application designers, data scientists, and system administrators will explore ways to create visualizations that bring context and a sense of time to streaming text data. Author Anthony Aragues guides you through the concepts and tools you need to build visualizations for analyzing data as it arrives. Determine your company’s goals for visualizing streaming data Identify key data sources and learn how to stream them Learn practical methods for processing streaming data Build a client application for interacting with events, logs, and records Explore common components for visualizing streaming data Consider analysis concepts for developing your visualization Define the dashboard’s layout, flow direction, and component movement Improve visualization quality and productivity through collaboration Explore use cases including security, IoT devices, and application data
Ai 2022 Advances In Artificial Intelligence
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Author : Haris Aziz
language : en
Publisher: Springer Nature
Release Date : 2022-12-02
Ai 2022 Advances In Artificial Intelligence written by Haris Aziz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-02 with Computers categories.
This book constitutes the refereed proceedings of the 35th Australasian Joint Conference on Artificial Intelligence, AI 2022, which took place in Perth, WA, Australia, in December 5–8, 2022. The 56 full papers included in this book were carefully reviewed and selected from 90 submissions. They were organized in topical sections as follows: Computer Vision; Deep Learning; Ethical/Explainable AI; Genetic Algorithms; Knowledge Representation and NLP; Machine Learning; Medical AI; Optimization; and Reinforcement Learning.
Towards Reliable Machine Learning In Evolving Data Streams
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Author : Johannes Haug
language : en
Publisher:
Release Date : 2022
Towards Reliable Machine Learning In Evolving Data Streams written by Johannes Haug and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Data streams are ubiquitous in many areas of modern life. For example, applications in healthcare, education, finance, or advertising often deal with large-scale and evolving data streams. Compared to stationary applications, data streams pose considerable additional challenges for automated decision making and machine learning. Indeed, online machine learning methods must cope with limited memory capacities, real-time requirements, and drifts in the data generating process. At the same time, online learning methods should provide a high predictive quality, stability in the presence of input noise, and good interpretability in order to be reliably used in practice. In this thesis, we address some of the most important aspects of machine learning in evolving data streams. Specifically, we identify four open issues related to online feature selection, concept drift detection, online classification, local explainability, and the evaluation of online learning methods. In these contexts, we present new theoretical and empirical findings as well as novel frameworks and implementations. In particular, we propose new approaches for online feature selection and concept drift detection that can account for model uncertainties and thus achieve more stable results. Moreover, we introduce a new incremental decision tree that retains valuable interpretability properties and a new change detection framework that allows for more efficient explanations based on local feature attributions. In fact, this is one of the first works to address intrinsic model interpretability and local explainability in the presence of incremental updates and concept drift. Along with this thesis, we provide extensive open resources related to online machine learning. Notably, we introduce a new Python framework that enables simplified and standardized evaluations and can thus serve as a basis for more comparable online learning experiments in the future. In total, this thesis is based on six publications, five of which were peer-reviewed at the time of publication of this thesis. Our work touches all major areas of predictive modeling in data streams and proposes novel solutions for efficient, stable, interpretable and thus reliable online machine learning.
Proceedings Of International Conference On Big Data Machine Learning And Applications
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Author : Ripon Patgiri
language : en
Publisher: Springer Nature
Release Date : 2021-03-22
Proceedings Of International Conference On Big Data Machine Learning And Applications written by Ripon Patgiri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-22 with Technology & Engineering categories.
This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings.