Supervised And Unsupervised Ensemble Methods And Their Applications PDF Download
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Author | : Oleg Okun |
Publisher | : Springer |
Total Pages | : 276 |
Release | : 2009-10-15 |
Genre | : Computers |
ISBN | : 3642039995 |
Download Applications of Supervised and Unsupervised Ensemble Methods Book in PDF, ePub and Kindle
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.
Author | : Oleg Okun |
Publisher | : Springer |
Total Pages | : 188 |
Release | : 2008-04-20 |
Genre | : Computers |
ISBN | : 3540789812 |
Download Supervised and Unsupervised Ensemble Methods and their Applications Book in PDF, ePub and Kindle
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.
Author | : Oleg Okun |
Publisher | : Springer Science & Business Media |
Total Pages | : 188 |
Release | : 2008-04-18 |
Genre | : Computers |
ISBN | : 3540789804 |
Download Supervised and Unsupervised Ensemble Methods and their Applications Book in PDF, ePub and Kindle
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.
Author | : Oleg Okun |
Publisher | : Springer |
Total Pages | : 262 |
Release | : 2011-09-01 |
Genre | : Technology & Engineering |
ISBN | : 3642229107 |
Download Ensembles in Machine Learning Applications Book in PDF, ePub and Kindle
This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.
Author | : Oleg Okun |
Publisher | : Springer Science & Business Media |
Total Pages | : 276 |
Release | : 2009-10-06 |
Genre | : Computers |
ISBN | : 3642039987 |
Download Applications of Supervised and Unsupervised Ensemble Methods Book in PDF, ePub and Kindle
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.
Author | : Zhi-Hua Zhou |
Publisher | : CRC Press |
Total Pages | : 238 |
Release | : 2012-06-06 |
Genre | : Business & Economics |
ISBN | : 1439830037 |
Download Ensemble Methods Book in PDF, ePub and Kindle
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.
Author | : Cha Zhang |
Publisher | : Springer Science & Business Media |
Total Pages | : 332 |
Release | : 2012-02-17 |
Genre | : Computers |
ISBN | : 1441993258 |
Download Ensemble Machine Learning Book in PDF, ePub and Kindle
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.
Author | : George Kyriakides |
Publisher | : Packt Publishing Ltd |
Total Pages | : 284 |
Release | : 2019-07-19 |
Genre | : Computers |
ISBN | : 178961788X |
Download Hands-On Ensemble Learning with Python Book in PDF, ePub and Kindle
Combine popular machine learning techniques to create ensemble models using Python Key FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
Author | : Michael W. Berry |
Publisher | : Springer Nature |
Total Pages | : 191 |
Release | : 2019-09-04 |
Genre | : Technology & Engineering |
ISBN | : 3030224759 |
Download Supervised and Unsupervised Learning for Data Science Book in PDF, ePub and Kindle
This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.
Author | : Michael J. Way |
Publisher | : CRC Press |
Total Pages | : 746 |
Release | : 2012-03-29 |
Genre | : Computers |
ISBN | : 143984173X |
Download Advances in Machine Learning and Data Mining for Astronomy Book in PDF, ePub and Kindle
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.