Learning Data Streams Evolving (23 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Condition: New.

Condition: New.

Taschenbuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.

Buch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.

Hardcover. Condition: Brand New. 328 pages. 9.25x6.10x1.02 inches. In Stock.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 328 pp. Englisch.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 328 pp. Englisch.

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides multiple examples to facilitate the understanding data streams in non-stationary environmentsPresents several application cases to show how the methods solve different real world problemsDiscusses the links between methods t.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides multiple examples to facilitate the understanding data streams in non-stationary environmentsPresents several application cases to show how the methods solve different real world problemsDiscusses the links between methods t.

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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is a significant contribution to the subject of mining time-changing data streams and addresses the design of learning algorithms for this purpose. It introduces new contributions on several different aspects of the problem, identifying research opportunities and increasing the scope for applications. It also includes an in-depth study of stream mining and a theoretical analysis of proposed methods and algorithms. The first section is concerned with the use of an adaptive sliding window algorithm (ADWIN). Since this has rigorous performance guarantees, using it in place of counters or accumulators, it offers the possibility of extending such guarantees to learning and mining algorithms not initially designed for drifting data. Testing with several methods, including Naïve Bayes, clustering, decision trees and ensemble methods, is discussed as well. The second part of the book describes a formal study of connected acyclic graphs, or 'trees', from the point of view of closure-based mining, presenting efficient algorithms for subtree testing and for mining ordered and unordered frequent closed trees. Lastly, a general methodology to identify closed patterns in a data stream is outlined. This is applied to develop an incremental method, a sliding-window based method, and a method that mines closed trees adaptively from data streams. These are used to introduce classification methods for tree data streams.