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Learning Systems, Eduard Aved`yan; J. Mason; P.C. Parks


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Автор: Eduard Aved`yan; J. Mason; P.C. Parks
Название:  Learning Systems
ISBN: 9783540199960
Издательство: Springer
Классификация:


ISBN-10: 3540199969
Обложка/Формат: Paperback
Страницы: 119
Вес: 0.20 кг.
Дата издания: 25.10.1995
Язык: English
Размер: 234 x 156 x 7
Основная тема: Engineering
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: A learning system can be defined as a system which can adapt its behaviour to become more effective at a particular task or set of tasks. This work covers major aspects of learning systems: system architecture, choice of performance index and methods measuring error.


The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 10480.00 р.
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Learning in Natural and Connectionist Systems

Автор: R.H. Phaf
Название: Learning in Natural and Connectionist Systems
ISBN: 0792326857 ISBN-13(EAN): 9780792326854
Издательство: Springer
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Цена: 25848.00 р.
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Описание: Modern research in neural networks has led to artificial learning systems, while work in the psychology of human memory has revealed much about how natural systems really learn, including the role of unconscious, implicit, memory processes. This book, combining the approaches, contributes to their mutual benefit.

Advances in Learning Classifier Systems

Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils
Название: Advances in Learning Classifier Systems
ISBN: 3540424377 ISBN-13(EAN): 9783540424376
Издательство: Springer
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Цена: 9781.00 р.
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Описание: These are the refereed post-proceedings of the Third International Workshop on Learning Classifier Systems, IWLCS 2000. The papers are organized in topical sections on theory, applications, and advanced architectures.

Learning Classifier Systems

Автор: Jaume Bacardit; Ester Bernad?-Mansilla; Martin V.
Название: Learning Classifier Systems
ISBN: 3540881379 ISBN-13(EAN): 9783540881377
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Covers post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - hosted by the Genetic and Evolutionary Computation Conference, GECCO. This book features sections on knowledge representation, and analysis of the system.

A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems

Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
ISBN: 1849968675 ISBN-13(EAN): 9781849968676
Издательство: Springer
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Цена: 23508.00 р.
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Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

Machine Learning and Systems Engineering

Автор: Sio-Iong Ao; Burghard B. Rieger; Mahyar Amouzegar
Название: Machine Learning and Systems Engineering
ISBN: 9400733747 ISBN-13(EAN): 9789400733749
Издательство: Springer
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Цена: 28732.00 р.
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Описание: A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009).

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Автор: Thorsten Wuest
Название: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
ISBN: 3319176102 ISBN-13(EAN): 9783319176109
Издательство: Springer
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Цена: 19564.00 р.
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Описание: The book reports on a novel approach for holistically identifying the relevant state drivers of complex, multi-stage manufacturing systems. In practice, this method can be used to identify the most important process parameters and state characteristics, the so-called state drivers, of a manufacturing system.

Machine Learning for Cyber Physical Systems

Автор: Oliver Niggemann; J?rgen Beyerer
Название: Machine Learning for Cyber Physical Systems
ISBN: 3662488361 ISBN-13(EAN): 9783662488362
Издательство: Springer
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Цена: 19591.00 р.
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Описание: Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment processcontrol.- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks.- Forecasting Cellular Connectivity for Cyber-Physical Systems: A Machine Learning Approach.- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation.- Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission.- Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases.- Towards a novel learning assistant for networked automation systems.- Effcient Image Processing System for an Industrial Machine Learning Task.- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation.- Geo-Distributed Analytics for the Internet of Things.- Implementation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation.- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency.- Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems.- Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.

Learning Classifier Systems

Автор: Pier Luca Lanzi; Wolfgang Stolzmann; Stewart W. Wi
Название: Learning Classifier Systems
ISBN: 3540205446 ISBN-13(EAN): 9783540205449
Издательство: Springer
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Цена: 9781.00 р.
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Описание: This book constitutes the refereed proceedings of the 5th International Workshop on Learning Classifier Systems, IWLCS 2003, held in Granada, Spain in September 2003 in conjunction with PPSN VII.

Adaptation and Learning in Multi-Agent Systems

Автор: Gerhard Wei?; Sandip Sen
Название: Adaptation and Learning in Multi-Agent Systems
ISBN: 3540609237 ISBN-13(EAN): 9783540609230
Издательство: Springer
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Цена: 6567.00 р.
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Описание: These conference proceedings describe and analyze, both experimentally and theoretically, new learning and adaption approaches for situations in which several agents have to co-operate or compete. A comprehensive introductory survey on the areas involved is included.

Machine Learning for Cyber Physical Systems

Автор: J?rgen Beyerer; Oliver Niggemann; Christian K?hner
Название: Machine Learning for Cyber Physical Systems
ISBN: 3662538059 ISBN-13(EAN): 9783662538050
Издательство: Springer
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Цена: 23757.00 р.
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Описание: The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, September 29th, 2016. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Algorithmic Learning for Knowledge-Based Systems

Автор: Klaus P. Jantke; Steffen Lange
Название: Algorithmic Learning for Knowledge-Based Systems
ISBN: 3540602178 ISBN-13(EAN): 9783540602170
Издательство: Springer
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Цена: 12577.00 р.
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Описание: This is the final report on a basic research project (GOSLER) on algorithmic learning for knowledge-based systems. The project focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation.


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