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Machine Learning and Knowledge Extraction, Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Ed


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Автор: Andreas Holzinger; Peter Kieseberg; A Min Tjoa; Ed
Название:  Machine Learning and Knowledge Extraction
ISBN: 9783319668079
Издательство: Springer
Классификация:

ISBN-10: 3319668072
Обложка/Формат: Paperback
Страницы: 376
Вес: 0.55 кг.
Дата издания: 24.08.2017
Серия: Information Systems and Applications, incl. Internet/Web, and HCI
Язык: English
Издание: 1st ed. 2017
Иллюстрации: 129 illustrations, black and white; xix, 374 p. 129 illus.
Размер: 234 x 156 x 21
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This new edition of the best-selling book focuses on various aspects of recruiting, including assessing an institution`s readiness to recruit international students, building human resource capacity for international recruitment, creating an international recruitment plan, recruiting international students from within the United States, measuring return on investment, and more.


Machine Learning

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
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Цена: 18622.00 р.
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Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning and Knowledge Discovery in Databases

Автор: Walter Daelemans; Katharina Morik
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3540874801 ISBN-13(EAN): 9783540874805
Издательство: Springer
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Цена: 16769.00 р.
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Описание: Covers the proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. This book addresses topics such as application of machine learning and data mining methods to real-world problems.

Machine Learning and Knowledge Discovery in Databases

Автор: Albert Bifet; Michael May; Bianca Zadrozny; Ricard
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319234609 ISBN-13(EAN): 9783319234601
Издательство: Springer
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Цена: 7826.00 р.
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Описание: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers.

Knowledge Representation and Organization in Machine Learning

Автор: Katharina Morik
Название: Knowledge Representation and Organization in Machine Learning
ISBN: 354050768X ISBN-13(EAN): 9783540507680
Издательство: Springer
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Цена: 7685.00 р.
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Описание: Machine learning has become a growing field of Artificial Intelligence. This book contains research papers that focus on the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. It is of interest to both experts and newcomers to the subject.

Knowledge Engineering, Machine Learning and Lattice Computing with Applications

Автор: Manuel Grana; Carlos Toro; Robert J. Howlett; Lakh
Название: Knowledge Engineering, Machine Learning and Lattice Computing with Applications
ISBN: 3642373429 ISBN-13(EAN): 9783642373428
Издательство: Springer
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Цена: 6429.00 р.
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Описание: This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012.

Machine Learning and Knowledge Discovery in Databases

Автор: Annalisa Appice; Pedro Pereira Rodrigues; V?tor Sa
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319235273 ISBN-13(EAN): 9783319235271
Издательство: Springer
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Цена: 12298.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Автор: Kelleher John D., Macnamee Brian, D`Arcy Aoife
Название: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
ISBN: 0262029448 ISBN-13(EAN): 9780262029445
Издательство: MIT Press
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Цена: 13543.00 р.
Наличие на складе: Нет в наличии.

Описание:

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

Towards Integrative Machine Learning and Knowledge Extraction

Автор: Andreas Holzinger; Randy Goebel; Massimo Ferri; Va
Название: Towards Integrative Machine Learning and Knowledge Extraction
ISBN: 3319697749 ISBN-13(EAN): 9783319697741
Издательство: Springer
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Цена: 7685.00 р.
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Описание: Towards integrative Machine Learning & Knowledge Extraction.- Machine Learning and Knowledge Extraction in Digital Pathology needs an integrative approach.- Comparison of Public-Domain Software and Services for Probabilistic Record Linkage and Address Standardization.- Better Interpretable Models for Proteomics Data Analysis Using rule-based Mining.- Probabilistic Logic Programming in Action.- Persistent topology for natural data analysis - A survey.- Predictive Models for Differentiation between Normal and Abnormal EEG through Cross-Correlation and Machine Learning Techniques.- A Brief Philosophical Note on Information.- Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline.- A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images.- Topological characteristics of oil and gas reservoirs and their applications.- Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment.

Machine Learning and Knowledge Discovery in Databases

Автор: Frasconi
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319462261 ISBN-13(EAN): 9783319462264
Издательство: Springer
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Цена: 13696.00 р.
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Описание: The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016.

Machine Learning and Knowledge Discovery in Databases

Автор: Berendt
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319461303 ISBN-13(EAN): 9783319461304
Издательство: Springer
Рейтинг:
Цена: 8106.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The three volume set LNAI 9851, LNAI 9852, and LNAI 9853 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2016, held in Riva del Garda, Italy, in September 2016.

Machine Learning and Knowledge Discovery in Databases

Автор: Annalisa Appice; Pedro Pereira Rodrigues; V?tor Sa
Название: Machine Learning and Knowledge Discovery in Databases
ISBN: 3319235249 ISBN-13(EAN): 9783319235240
Издательство: Springer
Рейтинг:
Цена: 12298.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers.

Data Analysis, Machine Learning and Knowledge Discovery

Автор: Myra Spiliopoulou; Lars Schmidt-Thieme; Ruth Janni
Название: Data Analysis, Machine Learning and Knowledge Discovery
ISBN: 331901594X ISBN-13(EAN): 9783319015941
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics.


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