Stochastic Optimization for Large-Scale Machine Learning, Chauhan Vinod Kumar
Автор: Tor Lattimore, Csaba Szepesvari Название: Bandit Algorithms ISBN: 1108486827 ISBN-13(EAN): 9781108486828 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.
Описание: With applications to many areas in science and technology, the technique examined in this volume is a highly effective algorithmic problem-solving tool. Contributions from leading experts cover the latest research and provide detailed examples of its use.
Автор: Marti, Kurt Название: Stochastic optimization methods ISBN: 3662500124 ISBN-13(EAN): 9783662500125 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Stochastic Optimization Methods.- Optimal Control Under Stochastic Uncertainty.- Stochastic Optimal Open-Loop Feedback Control.- Adaptive Optimal Stochastic Trajectory Planning and Control (AOSTPC).- Optimal Design of Regulators.- Expected Total Cost Minimum Design of Plane Frames.- Stochastic Structural Optimization with Quadratic Loss Functions.- Maximum Entropy Techniques.
Описание: Chapter two is dedicated to infinite horizon stochastic discrete optimal control models and Markov decision problems with average and expected total discounted optimization criteria, while Chapter three develops a special game-theoretical approach to Markov decision processes and stochastic discrete optimal control problems.
Автор: Xi-Ren Cao Название: Stochastic Learning and Optimization ISBN: 144194222X ISBN-13(EAN): 9781441942227 Издательство: Springer Рейтинг: Цена: 22563.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Performance optimization is vital in the design and operation of modern engineering systems. This book provides a unified framework based on a sensitivity point of view. It introduces new approaches and proposes new research topics.
Автор: Kurt Marti Название: Stochastic Optimization Methods ISBN: 3642098363 ISBN-13(EAN): 9783642098369 Издательство: Springer Рейтинг: Цена: 14635.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Optimization problems arising in practice involve random model parameters. This book features many illustrations, several examples, and applications to concrete problems from engineering and operations research.
Описание: Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities.
Автор: Richard Kipp Martin Название: Large Scale Linear and Integer Optimization: A Unified Approach ISBN: 1461372585 ISBN-13(EAN): 9781461372585 Издательство: Springer Рейтинг: Цена: 60983.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a textbook about linear and integer linear optimization. The major goal of the book is to develop the theory of linear and integer linear optimization in a unified manner and then demonstrate how to use this theory in a modern computing environment to solve very large real world problems.
Описание: This book details the designing of hybrid control strategies for practical systems containing time varying uncertainties, disturbances, nonlinearities, unknown parameters, unmodelled dynamics, delays, etc., concurrently. In this book, the advantages of different controllers will be brought together to produce superior control performance for the practical systems. Being aware of the advantages of adaptive controller to tackle unknown constant, time varying uncertainties and time varying disturbances, a variant of adaptive controller, namely L1 adaptive controller, is hybridized with other strategies. In this book, to facilitate optimal parameter setting of the basic L1 adaptive controller, stochastic optimization technique will be hybridized with it. The stability of the optimization technique along with the controller will be guaranteed analytically with the help of spectral radius convergence. The proposed method exhibits satisfactory exploration and exploitation capabilities. Again, this book will throw light on tackling nonlinearities along with uncertainties and disturbances by hybridizing fuzzy logic with L1 adaptive controller. The performances of the designed controllers will be compared with different control methodologies to validate their effectiveness. The overall stability of the nonlinear system with the designed controller will be guaranteed with the help of fuzzy Lyapunov function to retain the zonal behaviour of the system. This fuzzy PDC-L1 adaptive controller is efficient to tackle nonlinearities and at the same time cancels unknown constant, time varying uncertainties and time varying disturbances adequately. This book will also contain four simulation case studies to validate fruitfulness of the designed controllers. To demonstrate the superior control ability of these controllers in tackling practical system, three experimental case studies will also be provided.
Автор: Pardalos Название: Machine Learning, Optimization, and Big Data ISBN: 3319514687 ISBN-13(EAN): 9783319514680 Издательство: Springer Рейтинг: Цена: 8050.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes revised selected papers from the Second International Workshop on Machine Learning, Optimization, and Big Data, MOD 2016, held in Volterra, Italy, in August 2016. The 40 papers presented in this volume were carefully reviewed and selected from 97 submissions.
Описание: This book explores multidimensional particle swarm optimization, a technique developed by the authors and presented in a well-defined algorithmic approach. All featured applications are supported with fully documented source code as well as sample datasets.
Автор: Nicosia Название: Machine Learning, Optimization, and Big Data ISBN: 331972925X ISBN-13(EAN): 9783319729251 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the post-conference proceedings of the Third International Workshop on Machine Learning, Optimization, and Big Data, MOD 2017, held in Volterra, Italy, in September 2017. The 50 full papers presented were carefully reviewed and selected from 126 submissions.
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