Pattern Classifiers and Trainable Machines, J. Sklansky; G.N. Wassel
Автор: Ludmila I. Kuncheva Название: Combining Pattern Classifiers: Methods and Algorithms ISBN: 1118315235 ISBN-13(EAN): 9781118315231 Издательство: Wiley Рейтинг: Цена: 104490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Combined classifiers, which are central to the ubiquitous performance of pattern recognition and machine learning, are generally considered more accurate than single classifiers.
Автор: Michal Wozniak Название: Hybrid Classifiers ISBN: 3642409962 ISBN-13(EAN): 9783642409967 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book details how hybridization can help improve the quality of computer classification systems. It introduces the different levels of hybridization and illuminates common problems faced when dealing with such projects.
Автор: Michal Wozniak Название: Hybrid Classifiers ISBN: 3662523043 ISBN-13(EAN): 9783662523049 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book details how hybridization can help improve the quality of computer classification systems. It introduces the different levels of hybridization and illuminates common problems faced when dealing with such projects.
Автор: Sarunas Raudys Название: Statistical and Neural Classifiers ISBN: 1447110714 ISBN-13(EAN): 9781447110712 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used..
Автор: Josef Kittler; Fabio Roli Название: Multiple Classifier Systems ISBN: 3540422846 ISBN-13(EAN): 9783540422846 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
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: Larry Bull; Ester Bernad?-Mansilla; John Holmes Название: Learning Classifier Systems in Data Mining ISBN: 3642097758 ISBN-13(EAN): 9783642097751 Издательство: Springer Рейтинг: Цена: 130590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance of LCS in a variety of domains.
Автор: Jan Drugowitsch Название: Design and Analysis of Learning Classifier Systems ISBN: 3642098614 ISBN-13(EAN): 9783642098611 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition - derived from machine learning - of "a good set of cl- si?ers", based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of "good set of classi?ers" (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.
Автор: Martin V. Butz Название: Anticipatory Learning Classifier Systems ISBN: 1461352908 ISBN-13(EAN): 9781461352907 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior.
Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning.
Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system. It is an excellent reference for researchers interested in adaptive behavior and machine learning from a cognitive science perspective as well as those who are interested in combining evolutionary learning mechanisms for learning and optimization tasks.
Автор: Larry Bull Название: Applications of Learning Classifier Systems ISBN: 3642535593 ISBN-13(EAN): 9783642535598 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The field called Learning Classifier Systems is populated with romantics. The system embracing such a rule "popu- lation" would explore its available actions and responses, rewarding and rating the active rules accordingly.
Автор: Ludmila I. Kuncheva Название: Fuzzy Classifier Design ISBN: 3790824720 ISBN-13(EAN): 9783790824728 Издательство: Springer Рейтинг: Цена: 153720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In some books and journals the word fuzzy is religiously avoided: fuzzy set theory is viewed as a second-hand cheap trick whose aim is nothing else but to devalue good classical theories and open up the way to lazy ignorants and newcomers.
Автор: G.A. Lindeboom; A.A.G. Ham Название: A Classified Bibliography of the History of Dutch Medicine 1900–1974 ISBN: 9401181535 ISBN-13(EAN): 9789401181532 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In some periods of the past Netherlands medicine has played a major role in the evolution of European medicine; In this bibliography it has been my endeavour to compile references for all that has been written on the history of Dutch medicine in our country and elsewhere in our age.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
