Evolutionary Decision Trees in Large-Scale Data Mining, Marek Kretowski
Автор: Aljarah Ibrahim, Faris Hossam, Mirjalili Seyedali Название: Evolutionary Data Clustering: Algorithms and Applications ISBN: 9813341904 ISBN-13(EAN): 9789813341906 Издательство: Springer Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization.
Описание: This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms.
Описание: This book highlights cutting-edge applications of machine learning techniques for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables.
Описание: This, the 30th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six in-depth papers focusing on the subject of cloud computing. Topics covered within this context include cloud storage, model-driven development, informative modeling, and security-critical systems.
Автор: Kretowski Marek Название: Evolutionary Decision Trees in Large-Scale Data Mining ISBN: 3030218538 ISBN-13(EAN): 9783030218539 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents a unified framework, based on specialized evolutionary algorithms, for the global induction of various types of classification and regression trees from data. cost-sensitive model trees for financial data or multi-test trees for gene expression data.
Описание: Constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tubingen, Germany, in April 2009 co located with the Evo 2009 events. This book includes such topics as biomarker discovery, cell simulation and modeling, and ecological modeling.
Описание: This book explores hot topics in the design, administration and management of dynamic grid environments. The emphasis is on preferences and autonomous decisions of system users, secure access to processed data and services and the use of green technologies.
Автор: Ashish Ghosh Название: Evolutionary Computation in Data Mining ISBN: 3642421954 ISBN-13(EAN): 9783642421952 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge.
Описание: In this book the author discusses synergies between computers and thought, related to the field of Artificial Intelligence;
Автор: Pascal Bouvry; Horacio Gonz?lez-V?lez; Joanna Ko?o Название: Intelligent Decision Systems in Large-Scale Distributed Environments ISBN: 3662506866 ISBN-13(EAN): 9783662506868 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Readers will find here a wealth of analysis and a host of ideas relating to the implementation and evaluation of intelligent, next-generation techniques for solving complex decision making problems in tomorrow`s large-scale distributed computer systems.
Автор: Dunja Mladenic; Nada Lavra?; Marko Bohanec; Steve Название: Data Mining and Decision Support ISBN: 1461350042 ISBN-13(EAN): 9781461350040 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes a user-friendly, evolutionary algorithms-based framework for estimating data-driven models for a wide class of dynamical systems, including linear and nonlinear ones. The methodology addresses the problem of automating the process of estimating data-driven models from a user’s perspective. By combining elementary building blocks, it learns the dynamic relations governing the system from data, giving model estimates with various trade-offs, e.g. between complexity and accuracy. The evaluation of the method on a set of academic, benchmark and real-word problems is reported in detail. Overall, the book offers a state-of-the-art review on the problem of nonlinear model estimation and automated model selection for dynamical systems, reporting on a significant scientific advance that will pave the way to increasing automation in system identification.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
