Learning Systems, Eduard Aved`yan; J. Mason; P.C. Parks
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T 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.
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: MIT Press Рейтинг: Цена: 124150.00 T Наличие на складе: Невозможна поставка. 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.
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.
Автор: Jaume Bacardit; Ester Bernad?-Mansilla; Martin V. Название: Learning Classifier Systems ISBN: 3540881379 ISBN-13(EAN): 9783540881377 Издательство: Springer Рейтинг: Цена: 65210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems ISBN: 1849968675 ISBN-13(EAN): 9781849968676 Издательство: Springer Рейтинг: Цена: 156720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Sio-Iong Ao; Burghard B. Rieger; Mahyar Amouzegar Название: Machine Learning and Systems Engineering ISBN: 9400733747 ISBN-13(EAN): 9789400733749 Издательство: Springer Рейтинг: Цена: 191550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Thorsten Wuest Название: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning ISBN: 3319176102 ISBN-13(EAN): 9783319176109 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Oliver Niggemann; J?rgen Beyerer Название: Machine Learning for Cyber Physical Systems ISBN: 3662488361 ISBN-13(EAN): 9783662488362 Издательство: Springer Рейтинг: Цена: 130610.00 T 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: R.H. Phaf Название: Learning in Natural and Connectionist Systems ISBN: 0792326857 ISBN-13(EAN): 9780792326854 Издательство: Springer Рейтинг: Цена: 172320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Pier Luca Lanzi; Wolfgang Stolzmann; Stewart W. Wi Название: Learning Classifier Systems ISBN: 3540205446 ISBN-13(EAN): 9783540205449 Издательство: Springer Рейтинг: Цена: 65210.00 T 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: Pier L. Lanzi; Wolfgang Stolzmann; Stewart W. Wils Название: Advances in Learning Classifier Systems ISBN: 3540424377 ISBN-13(EAN): 9783540424376 Издательство: Springer Рейтинг: Цена: 65210.00 T 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: Gerhard Wei?; Sandip Sen Название: Adaptation and Learning in Multi-Agent Systems ISBN: 3540609237 ISBN-13(EAN): 9783540609230 Издательство: Springer Рейтинг: Цена: 43780.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
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