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Applications of Supervised and Unsupervised Ensemble Methods

Applications of Supervised and Unsupervised Ensemble Methods PDF Author: Oleg Okun
Publisher: Springer Science & Business Media
ISBN: 3642039987
Category : Computers
Languages : en
Pages : 268

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Book Description
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.

Applications of Supervised and Unsupervised Ensemble Methods

Applications of Supervised and Unsupervised Ensemble Methods PDF Author: Oleg Okun
Publisher: Springer Science & Business Media
ISBN: 3642039987
Category : Computers
Languages : en
Pages : 268

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Book Description
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.

Ensemble Methods

Ensemble Methods PDF Author: Zhi-Hua Zhou
Publisher: CRC Press
ISBN: 1439830053
Category : Business & Economics
Languages : en
Pages : 236

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Book Description
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.

Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods PDF Author:
Publisher:
ISBN: 9814468312
Category :
Languages : en
Pages :

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Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition)

Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) PDF Author: Lior Rokach
Publisher: World Scientific
ISBN: 9811201978
Category : Computers
Languages : en
Pages : 300

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Book Description
This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

Supervised and Unsupervised Ensemble Methods and their Applications

Supervised and Unsupervised Ensemble Methods and their Applications PDF Author: Oleg Okun
Publisher: Springer Science & Business Media
ISBN: 3540789804
Category : Computers
Languages : en
Pages : 182

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Book Description
This book results from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) in June 2007 in Girona, Spain. This workshop was held alongside the 3rd Iberian Conference on Pattern Recognition and Image Analysis.

Ensemble Learning Algorithms With Python

Ensemble Learning Algorithms With Python PDF Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 450

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Book Description
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.

Decision Tree and Ensemble Learning Based on Ant Colony Optimization

Decision Tree and Ensemble Learning Based on Ant Colony Optimization PDF Author: Jan Kozak
Publisher: Springer
ISBN: 3319937529
Category : Technology & Engineering
Languages : en
Pages : 159

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Book Description
This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.

Methods and Applications in Molecular Phylogenetics

Methods and Applications in Molecular Phylogenetics PDF Author: Juan Wang
Publisher: Frontiers Media SA
ISBN: 2889763447
Category : Science
Languages : en
Pages : 107

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Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook PDF Author: Oded Maimon
Publisher: Springer Science & Business Media
ISBN: 9780387244358
Category : Computers
Languages : en
Pages : 1383

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Book Description
Organizes major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). This book provides algorithmic descriptions of classic methods, and also suitable for professionals in fields such as computing applications, information systems management, and more.

Particle Characterization: Light Scattering Methods

Particle Characterization: Light Scattering Methods PDF Author: Renliang Xu
Publisher: Springer Science & Business Media
ISBN: 0306471248
Category : Science
Languages : en
Pages : 399

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Book Description
Particle characterization is an important component in product research and development, manufacture, and quality control of particulate materials and an important tool in the frontier of sciences, such as in biotechnology and nanotechnology. This book systematically describes one major branch of modern particle characterization technology - the light scattering methods. This is the first monograph in particle science and technology covering the principles, instrumentation, data interpretation, applications, and latest experimental development in laser diffraction, optical particle counting, photon correlation spectroscopy, and electrophoretic light scattering. In addition, a summary of all major particle sizing and other characterization methods, basic statistics and sample preparation techniques used in particle characterization, as well as almost 500 latest references are provided. The book is a must for industrial users of light scattering techniques characterizing a variety of particulate systems and for undergraduate or graduate students who want to learn how to use light scattering to study particular materials, in chemical engineering, material sciences, physical chemistry and other related fields.