Ensembles In Machine Learning Applications
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Ensembles In Machine Learning Applications
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Author : Oleg Okun
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-09-07
Ensembles In Machine Learning Applications written by Oleg Okun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-07 with Computers categories.
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.
Deep Learning Applications In Translational Bioinformatics
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Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-03-07
Deep Learning Applications In Translational Bioinformatics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-07 with Technology & Engineering categories.
Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, various applications of deep learning in translational bioinformatics including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models. - Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges - Presents integrative and multidisciplinary approaches to ubiquitous healthcare - Includes case studies to illustrate the concepts discussed
Ensemble Classification Methods With Applications In R
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Author : Esteban Alfaro
language : en
Publisher: John Wiley & Sons
Release Date : 2018-08-15
Ensemble Classification Methods With Applications In R written by Esteban Alfaro and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-15 with Mathematics categories.
An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classification has become an extension of the individual classifiers. The text puts the emphasis on two areas of machine learning: classification trees and ensemble learning. The authors explore ensemble classification methods’ basic characteristics and explain the types of problems that can emerge in its application. Written by a team of noted experts in the field, the text is divided into two main sections. The first section outlines the theoretical underpinnings of the topic and the second section is designed to include examples of practical applications. The book contains a wealth of illustrative cases of business failure prediction, zoology, ecology and others. This vital guide: Offers an important text that has been tested both in the classroom and at tutorials at conferences Contains authoritative information written by leading experts in the field Presents a comprehensive text that can be applied to courses in machine learning, data mining and artificial intelligence Combines in one volume two of the most intriguing topics in machine learning: ensemble learning and classification trees Written for researchers from many fields such as biostatistics, economics, environment, zoology, as well as students of data mining and machine learning, Ensemble Classification Methods with Applications in R puts the focus on two topics in machine learning: classification trees and ensemble learning.
Ensemble Machine Learning
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Author : Cha Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-17
Ensemble Machine Learning written by Cha Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-17 with Computers categories.
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 For Machine Learning
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Author : Gautam Kunapuli
language : en
Publisher: Simon and Schuster
Release Date : 2023-05-30
Ensemble Methods For Machine Learning written by Gautam Kunapuli and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-30 with Computers categories.
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
Applications Of Supervised And Unsupervised Ensemble Methods
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Author : Oleg Okun
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-10-06
Applications Of Supervised And Unsupervised Ensemble Methods written by Oleg Okun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-06 with Computers categories.
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.
Artificial Intelligence For Next Generation Computing Volume 1
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Author : Sanjay Kumar
language : en
Publisher: Springer Nature
Release Date : 2026-01-02
Artificial Intelligence For Next Generation Computing Volume 1 written by Sanjay Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-02 with Computers categories.
This book features proceedings of the 2nd International Conference on Artificial Intelligence, Computing Technologies, Internet of Things (IoT), and Data Analytics – AICTA 2024. The theme of the conference is “Artificial Intelligence and its applications.” It focuses on recent trends and innovative approaches in the different domains of Computer Engineering like cloud computing, image processing and computer vision, machine learning,g and deep learning, IoT and analytics, security, etc. The book introduces new ideas in artificial intelligence and its subset technologies like machine learning, deep neural networks, etc. This volume will be useful for researchers and practitioners working in computer engineering and related areas.
Ensemble Algorithms And Their Applications
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Author : Panagiotis E. Pintelas
language : en
Publisher:
Release Date : 2020
Ensemble Algorithms And Their Applications written by Panagiotis E. Pintelas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
In recent decades, the development of ensemble learning methodologies has gained a significant attention from the scientific and industrial community, and found their application in various real-word problems. Theoretical and experimental evidence proved that ensemble models provide a considerably better prediction performance than single models. The main aim of this collection is to present the recent advances related to ensemble learning algorithms and investigate the impact of their application in a diversity of real-world problems. All papers possess significant elements of novelty and introduce interesting ensemble-based approaches, which provide readers with a glimpse of the state-of-the-art research in the domain.
Proceedings Of Data Analytics And Management
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Author : Abhishek Swaroop
language : en
Publisher: Springer Nature
Release Date : 2025-11-06
Proceedings Of Data Analytics And Management written by Abhishek Swaroop and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-06 with Computers categories.
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2025), held at London Metropolitan University, London, UK, during June 2025. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. The book is divided into ten volumes.
Intelligent Systems And Applications
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2025-09-02
Intelligent Systems And Applications written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-02 with Computers categories.
The 11th Intelligent Systems Conference (IntelliSys) 2025, held in Amsterdam, The Netherlands, from 28–29 August 2025, brought together researchers, practitioners, and experts from around the world to share advancements in intelligent technologies. Conducted in a hybrid format, the conference facilitated global collaboration and participation. This volume presents a curated selection of 169 peer-reviewed papers from a total of 470 submissions, covering key areas such as Artificial Intelligence, Computer Vision, Robotics, and Intelligent Systems. The contributions reflect the latest research trends, practical applications, and emerging challenges in these domains. We hope that these proceedings serve as a valuable resource for researchers, practitioners, and students, and that they inspire future work and collaborations in the field of intelligent systems.