Decision Tree and Ensemble Learning Based on Ant Colony Optimization, Jan Kozak

Автор: Oleg Okun
Название: Supervised and Unsupervised Ensemble Methods and their Applications
ISBN: 3540789804 ISBN-13(EAN): 9783540789802
Издательство: Springer
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Цена: 139750.00 T
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Описание: Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Ensembles of Classification Methods and Their Applications.- Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.- Erratum.

Автор: Yang Yun
Название: Temporal Data Mining via Unsupervised Ensemble Learning
ISBN: 0128116544 ISBN-13(EAN): 9780128116548
Издательство: Elsevier Science
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Цена: 48270.00 T
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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.

Автор: Oleg Okun
Название: Applications of Supervised and Unsupervised Ensemble Methods
ISBN: 3642039987 ISBN-13(EAN): 9783642039980
Издательство: Springer
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Цена: 139750.00 T
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Описание: Expanding upon presentations at last year`s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.

Автор: Soto Jesus, Melin Patricia, Castillo Oscar
Название: Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction
ISBN: 3319712632 ISBN-13(EAN): 9783319712635
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error.

Автор: David A. Pelta; Carlos Cruz Corona
Название: Soft Computing Based Optimization and Decision Models
ISBN: 3319877631 ISBN-13(EAN): 9783319877631
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book offers a timely snapshot of current soft-computing research and solutions to decision-making and optimization problems, which are ubiquitous in the current social and technological context, addressing fields including logistics, transportation and data analysis.  Written by leading international experts from the United States, Brazil and Cuba, as well as the United Kingdom, France, Finland and Spain, it discusses theoretical developments in and practical applications of soft computing in fields where these methods are crucial to obtaining better models, including: intelligent transportation systems, maritime logistics, portfolio selection, decision- making, fuzzy cognitive maps, and fault detection.  The book is dedicated to Professor Jos? L. Verdegay, a pioneer who has been actively pursuing research in fuzzy sets theory and soft computing since 1982, in honor of his 65th birthday.

Автор: Bergman, David
Название: Decision Diagrams for Optimization
ISBN: 3319428470 ISBN-13(EAN): 9783319428475
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book introduces a novel approach to discrete optimization, providing both theoretical insights and algorithmic developments that lead to improvements over state-of-the-art technology. The authors present chapters on the use of decision diagrams for combinatorial optimization and constraint programming, with attention to general-purpose solution methods as well as problem-specific techniques.The book will be useful for researchers and practitioners in discrete optimization and constraint programming. 'Decision Diagrams for Optimization is one of the most exciting developments emerging from constraint programming in recent years. This book is a compelling summary of existing results in this space and a must-read for optimizers around the world.' [Pascal Van Hentenryck]

Автор: Bruce L. Golden; S. Raghavan; Edward A. Wasil
Название: The Next Wave in Computing, Optimization, and Decision Technologies
ISBN: 1461498511 ISBN-13(EAN): 9781461498513
Издательство: Springer
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Цена: 186340.00 T
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Описание: Computer Science and Operations Research continue to have a synergistic relationship and this book represents the results of the cross-fertilization between OR/MS and CS/AI.

Автор: Christer Carlsson; Robert Fuller
Название: Fuzzy Reasoning in Decision Making and Optimization
ISBN: 3790824976 ISBN-13(EAN): 9783790824971
Издательство: Springer
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Цена: 194730.00 T
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Описание: Unlike classical logic which requires a deep under- standing of a system, exact equations, and precise numeric values, fuzzy logic incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience.

Автор: Leticia Amador; Oscar Castillo
Название: Optimization of Type-2 Fuzzy Controllers Using the Bee Colony Algorithm
ISBN: 331954294X ISBN-13(EAN): 9783319542942
Издательство: Springer
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Цена: 69870.00 T
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Описание: For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.

Автор: Zhou
Название: Combustion Optimization Based on Computational Intelligence
ISBN: 9811078734 ISBN-13(EAN): 9789811078736
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book presents the latest findings on the subject of combustion optimization based on computational intelligence. It also describes the optimization of combustion parameters using genetic algorithms or ant colony algorithms, an online coal optimization system, etc.

Автор: Muhammet ?nal; Ay?a Ak; Vedat Topuz; Hasan Erdal
Название: Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
ISBN: 3642434770 ISBN-13(EAN): 9783642434778
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book covers the theory behind artificial neural networks, genetic algorithms and the ant colony optimization algorithm, and presents a novel real time control algorithm using genetic and ant colony optimization algorithms for optimizing PID controllers.

Автор: Yujun Zheng; Xueqin Lu; Minxia Zhang; Shengyong Ch
Название: Biogeography-Based Optimization: Algorithms and Applications
ISBN: 9811325855 ISBN-13(EAN): 9789811325854
Издательство: Springer
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Цена: 111790.00 T
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Описание: This book introduces readers to the background, general framework, main operators, and other basic characteristics of biogeography-based optimization (BBO), which is an emerging branch of bio-inspired computation. In particular, the book presents the authors’ recent work on improved variants of BBO, hybridization of BBO with other algorithms, and the application of BBO to a variety of domains including transportation, image processing, and neural network learning. The content will help to advance research into and application of not only BBO but also the whole field of bio-inspired computation. The algorithms and applications are organized in a step-by-step manner and clearly described with the help of pseudo-codes and flowcharts. The readers will learn not only the basic concepts of BBO but also how to apply and adapt the algorithms to the engineering optimization problems they actually encounter.