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Machine Learning in Non-stationary Environments

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Author :
Release : 2012
Genre : Computers
Kind : eBook
Book Rating : 091/5 ( reviews)

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Book Synopsis Machine Learning in Non-stationary Environments by : Masashi Sugiyama

Download or read book Machine Learning in Non-stationary Environments written by Masashi Sugiyama. This book was released on 2012. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with non-stationarity is one of modem machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.

Learning in Non-Stationary Environments

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Author :
Release : 2012-04-13
Genre : Technology & Engineering
Kind : eBook
Book Rating : 202/5 ( reviews)

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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. This book was released on 2012-04-13. 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 in Non-Stationary Environments

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Release :
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Machine Learning in Non-Stationary Environments by : Motoaki Kawanabe

Download or read book Machine Learning in Non-Stationary Environments written by Motoaki Kawanabe. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity.

Machine Learning in Non-stationary Environments

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Author :
Release : 2020
Genre : Computational intelligence
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Machine Learning in Non-stationary Environments by : Yi He

Download or read book Machine Learning in Non-stationary Environments written by Yi He. This book was released on 2020. Available in PDF, EPUB and Kindle. Book excerpt:

Adapting Machine Learning to Non-stationary Environments

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Author :
Release : 2023-04-04
Genre : Computers
Kind : eBook
Book Rating : 950/5 ( reviews)

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Book Synopsis Adapting Machine Learning to Non-stationary Environments by : Wintheiser Donnie

Download or read book Adapting Machine Learning to Non-stationary Environments written by Wintheiser Donnie. This book was released on 2023-04-04. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning stimulates a broad range of computational methods that exploit experience, which typically takes the form of electronic data, to make profitable decisions or accurate predictions. To date, the machine learning models have been applied to extensive application domains across diverse fields, including but not limited to computer vision [1, 2, 3], natural language processing [4, 5, 6], robotic control [7, 8], and cyber security [9, 10, 11].

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