Learning Classifier Systems, Pier Luca Lanzi; Wolfgang Stolzmann; Stewart W. Wi

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.

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.

Автор: Josef Kittler; Fabio Roli
Название: Multiple Classifier Systems
ISBN: 3540422846 ISBN-13(EAN): 9783540422846
Издательство: Springer
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Цена: 12157.00 р.
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Автор: Neamat El Gayar; Josef Kittler; Fabio Roli
Название: Multiple Classifier Systems
ISBN: 3642121268 ISBN-13(EAN): 9783642121265
Издательство: Springer
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Цена: 10480.00 р.
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Описание: Constitutes the proceedings of the 9th International Workshop on Multiple Classifier Systems, MCS 2010, held in Cairo, Egypt, in April 2010. This book includes contributions that are organized into sessions dealing with classifier combination and classifier selection, diversity, bagging and boosting, and combination of multiple kernels.

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).

Автор: Eduard Aved`yan; J. Mason; P.C. Parks
Название: Learning Systems
ISBN: 3540199969 ISBN-13(EAN): 9783540199960
Издательство: Springer
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Цена: 12157.00 р.
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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.

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.

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.

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.