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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.

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

View

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.

Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods PDF Author: Lior Rokach
Publisher: World Scientific
ISBN: 9814271063
Category : Computers
Languages : en
Pages : 242

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Book Description
Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.

Pattern Classification Using Ensemble Methods

Pattern Classification Using Ensemble Methods PDF Author: Lior Rokach
Publisher: World Scientific
ISBN: 9814271071
Category : Computers
Languages : en
Pages : 242

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Book Description
1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning - post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I - training an ensemble given a code-matrix. 6.3. Type II - adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used?

Ensemble Learning

Ensemble Learning PDF Author: Lior Rokach
Publisher:
ISBN: 9789811201967
Category : COMPUTERS
Languages : en
Pages : 301

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Book Description


Combining Pattern Classifiers

Combining Pattern Classifiers PDF Author: Ludmila I. Kuncheva
Publisher: John Wiley & Sons
ISBN: 1118315235
Category : Technology & Engineering
Languages : en
Pages : 384

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Book Description
A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.

Evolutionary Optimization and Ensemble Techniques for Data Mining and Pattern Recognition

Evolutionary Optimization and Ensemble Techniques for Data Mining and Pattern Recognition PDF Author: Alexander P. Topchy
Publisher: Ann Arbor, Mich. : University Microfilms International
ISBN:
Category : Data mining
Languages : en
Pages : 344

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Book Description


Ensemble Machine Learning

Ensemble Machine Learning PDF Author: Cha Zhang
Publisher: Springer Science & Business Media
ISBN: 1441993258
Category : Computers
Languages : en
Pages : 332

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Book Description
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Ensemble Methods

Ensemble Methods PDF Author: Zhi-Hua Zhou
Publisher: CRC Press
ISBN: 1439830037
Category : Business & Economics
Languages : en
Pages : 238

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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.

Multi-class Pattern Classification Using Neural Networks

Multi-class Pattern Classification Using Neural Networks PDF Author: Guobin Ou
Publisher:
ISBN:
Category : Computer network architectures
Languages : en
Pages : 128

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Book Description


Applications of Supervised and Unsupervised Ensemble Methods

Applications of Supervised and Unsupervised Ensemble Methods PDF Author: Oleg Okun
Publisher: Springer
ISBN: 3642039995
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.