Adaptation and Learning in Multi-Agent Systems, Gerhard Wei?; Sandip Sen
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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.
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.
Описание: This report documents current and ongoing developments in the area of learning in distributed artificial intelligence systems. The interdisciplinary co-operation of researchers from DAI and machine learning has established an active area of research and development.
Автор: Schwartz H M Название: Multi-Agent Machine Learning ISBN: 111836208X ISBN-13(EAN): 9781118362082 Издательство: Wiley Рейтинг: Цена: 15198.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces.
Автор: Oliver Niggemann; J?rgen Beyerer Название: Machine Learning for Cyber Physical Systems ISBN: 3662488361 ISBN-13(EAN): 9783662488362 Издательство: Springer Рейтинг: Цена: 19591.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: J?rgen Beyerer; Oliver Niggemann; Christian K?hner Название: Machine Learning for Cyber Physical Systems ISBN: 3662538059 ISBN-13(EAN): 9783662538050 Издательство: Springer Рейтинг: Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540424377 ISBN-13(EAN): 9783540424376 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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: Pier Luca Lanzi; Wolfgang Stolzmann; Stewart W. Wi Название: Learning Classifier Systems ISBN: 3540205446 ISBN-13(EAN): 9783540205449 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Eduard Aved`yan; J. Mason; P.C. Parks Название: Learning Systems ISBN: 3540199969 ISBN-13(EAN): 9783540199960 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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