Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II, Paolo Arena; Luca Patan?
Автор: Yang Yun Название: Temporal Data Mining via Unsupervised Ensemble Learning ISBN: 0128116544 ISBN-13(EAN): 9780128116548 Издательство: Elsevier Science Рейтинг: Цена: 7241.00 р. Наличие на складе: Поставка под заказ.
Описание: Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. . Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. . Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.
Описание: This volume collects significant results of the research Project called "SPARK". Its aim was to develop new cognitive architectures and sensing-perceiving-moving artefacts, inspired by the basic principles of living systems and based on "self-organization".
Автор: Claudia Bauzer Medeiros; Max Egenhofer; Elisa Bert Название: Advances in Spatial and Temporal Databases ISBN: 3540281274 ISBN-13(EAN): 9783540281276 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Includes the refereed proceedings of the 9th International Symposium on Spatial and Temporal Databases, SSTD 2005, held in Angra dos Reis, Brazil in August 2005. This book offers topical sections on query optimization and simulation, advanced query processing, spatial/temporal data streams, indexing schemes, novel applications and more.
Описание: In this thesis, the author develops for the first time an implementation methodology for arbitrary Gaussian operations using temporal-mode cluster states. The experimental basic structure of one-way quantum computation over two-mode input state is demonstrated by the controlled-Z gate and the optimum nonlocal gate experiments.
Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
Автор: Gabriele Puppis Название: Automata for Branching and Layered Temporal Structures ISBN: 3642118801 ISBN-13(EAN): 9783642118807 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Since 2002, FoLLI awards an annual prize for an outstanding dissertation in the fields of Logic, Language, and Information. This book focuses on Logic and Computation and, more specifically, on automata-based decidability techniques for time granularity and on a fresh method for deciding Monadic Second Order theories of trees.
Автор: Douzal-Chouakria Название: Advanced Analysis and Learning on Temporal Data ISBN: 3319444115 ISBN-13(EAN): 9783319444116 Издательство: Springer Рейтинг: Цена: 5870.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The last part of the book is dedicated to metric learning and time series comparison, it addresses the problem of speeding-up the dynamic time warping or dealing with multi-modal and multi-scale metric learning for time series classification and clustering.
Описание: This volume provides a brief review of the previous work on model reduction and identification of DPS, and develops new spatio-temporal models and their relevant identification approaches. All modeling approaches are applied to industrial thermal processes.
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