The Nested Universal Relation Database Model, Mark Levene
Автор: Leyuan Shi; Sigurdur ?lafsson Название: Nested Partitions Method, Theory and Applications ISBN: 1441944206 ISBN-13(EAN): 9781441944207 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: There is an ever-increasing need to solve optimization problems in a wide variety of science and engineering applications. This up-to-date and highly relevant book provides a cutting-edge research tool to use for large-scale, complex systems optimization.
Автор: Rainer Thiel Название: Nested Games of External Democracy Promotion ISBN: 3531177699 ISBN-13(EAN): 9783531177694 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Serge Abiteboul; Patrick C. Fischer; Hans-J?rg Sch Название: Nested Relations and Complex Objects in Databases ISBN: 3540511717 ISBN-13(EAN): 9783540511717 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Steven L. Salzberg Название: Learning with Nested Generalized Exemplars ISBN: 0792391101 ISBN-13(EAN): 9780792391104 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine Learning is one of the oldest and most intriguing areas of Ar- tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in- clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di- agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica- tion areas.
Автор: Robert John Nicholas Baldock Название: Classical Statistical Mechanics with Nested Sampling ISBN: 3319667688 ISBN-13(EAN): 9783319667683 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This thesis develops a nested sampling algorithm into a black box tool for directly calculating the partition function, and thus the complete phase diagram of a material, from the interatomic potential energy function.
Автор: Christian Walloth Название: Emergent Nested Systems ISBN: 3319275488 ISBN-13(EAN): 9783319275482 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Complex Systems and Man's Desire to Understand and Influence Them.- A Theory of Emergent Nested Systems.- Understanding and Influencing Emergent Nested Systems.- Reflections and Outlook.
Описание: The development of corporate database systems is complex, time-consuming, and expensive, causing developers to look for ways to cut costs. Len Silverston found a way to do this by identifying core data models that most companies share, standardizing them, and making them available on this CD-ROM.
Автор: Steven L. Salzberg Название: Learning with Nested Generalized Exemplars ISBN: 1461288304 ISBN-13(EAN): 9781461288305 Издательство: Springer Рейтинг: Цена: 17749.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine Learning is one of the oldest and most intriguing areas of Ar- tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in- clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di- agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica- tion areas.
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