Machine Learning and Data Mining in Aerospace Technology, Aboul Ella Hassanien, Ashraf Darwish

Автор: Clara Pizzuti; Marylyn D. Ritchie; Mario Giacobini
Название: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
ISBN: 3642011837 ISBN-13(EAN): 9783642011832
Издательство: Springer
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Цена: 65210.00 T
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Описание: Constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tubingen, Germany, in April 2009 co located with the Evo 2009 events. This book includes such topics as biomarker discovery, cell simulation and modeling, and ecological modeling.

Автор: Bertrand Clarke; Ernest Fokoue; Hao Helen Zhang
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 1461417074 ISBN-13(EAN): 9781461417071
Издательство: Springer
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Цена: 144410.00 T
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Описание: This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.

Автор: Clarke
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 0387981349 ISBN-13(EAN): 9780387981345
Издательство: Springer
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Цена: 186330.00 T
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Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering

Автор: Miroslav Kubat
Название: An Introduction to Machine Learning
ISBN: 3319348868 ISBN-13(EAN): 9783319348865
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Joseph J. S. Shang, Sergey T. Surzhikov
Название: Plasma Dynamics for Aerospace Engineering
ISBN: 110841897X ISBN-13(EAN): 9781108418973
Издательство: Cambridge Academ
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Цена: 115110.00 T
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Описание: This volume establishes the foundation and best practices for applying plasma dynamics to viable aerospace engineering plasma applications, offering a comprehensive review while providing directly usable problem-solving techniques. It bridges the gap between theoretical physics and modeling and simulation technique for studying plasma dynamics.

Автор: Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319419196 ISBN-13(EAN): 9783319419190
Издательство: Springer
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Цена: 89440.00 T
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Описание: 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.

Автор: Shibiao Wan,Man-Wai Mak
Название: Machine Learning for Protein Subcellular Localization Prediction
ISBN: 1501510487 ISBN-13(EAN): 9781501510489
Издательство: Walter de Gruyter
Цена: 86720.00 T
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Описание: Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.

Автор: Marcus A. Maloof
Название: Machine Learning and Data Mining for Computer Security
ISBN: 1849965447 ISBN-13(EAN): 9781849965446
Издательство: Springer
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Цена: 130430.00 T
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Описание: "Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security.

Автор: Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319961357 ISBN-13(EAN): 9783319961354
Издательство: Springer
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Цена: 46570.00 T
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Автор: Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3319961322 ISBN-13(EAN): 9783319961323
Издательство: Springer
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Цена: 68930.00 T
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Описание: The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Автор: Manfred Stede, Jodi Schneider
Название: Argumentation Mining
ISBN: 1681734613 ISBN-13(EAN): 9781681734613
Издательство: Mare Nostrum (Eurospan)
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Цена: 97950.00 T
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Описание: Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others.The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity.Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches.Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields. We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text.The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a-necessarily subjective-outlook for the field.