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Books on the topic 'Ensemble learning method'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Zhang, Cha. Ensemble Machine Learning: Methods and Applications. Springer US, 2012.

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2

Baruque, Bruno, and Emilio Corchado. Fusion Methods for Unsupervised Learning Ensembles. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16205-3.

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3

Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Springer, 2010.

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4

Club, Rocket Baby. Poisonous Berries : Machine Learning for Kids: Ensemble Method. Primedia eLaunch LLC, 2019.

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5

Rokach, Lior. Ensemble Learning: Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2019.

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6

Ensemble Methods for Machine Learning. Manning Publications Co. LLC, 2022.

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7

Ensemble Methods for Machine Learning. Manning Publications Co. LLC, 2023.

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8

Zhou, Zhi-Hua. Ensemble Methods: Foundations and Algorithms. Taylor & Francis Group, 2012.

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9

Zhou, Zhi-Hua. Ensemble Methods: Foundations and Algorithms. Taylor & Francis Group, 2012.

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10

Zhou, Zhi-Hua. Ensemble Methods: Foundations and Algorithms. Taylor & Francis Group, 2012.

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11

Rokach, Lior. Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2009.

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12

Zhang, Cha, and Yunqian Ma. Ensemble Machine Learning: Methods and Applications. Springer New York, 2014.

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13

Zhang, Cha, and Yunqian Ma. Ensemble Machine Learning: Methods and Applications. Springer, 2012.

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14

Zhang, Cha, and Yunqian Ma. Ensemble Machine Learning: Methods and Applications. Springer, 2012.

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15

Pattern Classification Using Ensemble Methods. World Scientific Publishing Co Pte Ltd, 2009.

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16

Pattern classification using ensemble methods. World Scientific, 2010.

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17

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2010.

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18

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.

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19

Alfaro, Esteban, Matías Gámez, and Noelia García. Ensemble Classification Methods with Applications in R. Wiley & Sons, Limited, John, 2018.

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20

Alfaro, Esteban, Matías Gámez, and Noelia García. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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21

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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22

Alfaro, Esteban, Mat�as G�mez, and Noelia Garc�a. Ensemble Classification Methods with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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23

Applications Of Supervised And Unsupervised Ensemble Methods. Springer, 2009.

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24

Okun, Oleg. Supervised and Unsupervised Ensemble Methods and Their Applications. Springer London, Limited, 2008.

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25

Okun, Oleg. Supervised and Unsupervised Ensemble Methods and their Applications. Springer, 2010.

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26

Okun, Oleg. Applications of Supervised and Unsupervised Ensemble Methods. Springer, 2009.

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27

Okun, Oleg. Applications of Supervised and Unsupervised Ensemble Methods. Springer Berlin / Heidelberg, 2012.

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28

Ensemble methods: Foundations and algorithms. Taylor & Francis, 2012.

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29

Shaw, Brian P. Music Assessment for Better Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190603144.001.0001.

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Assessment is central to ensemble music. Yet, teachers do not always have the expertise to harness its potential to improve rehearsals and performances, and promote and document student learning. Written specifically for band, choir, and orchestra teachers at all levels, this book contains all of the information necessary to design and use assessment in a thriving music classroom. The first section addresses foundations such as learning targets, metacognition, and growth mindset. Assessment jargon such as formative assessment, summative assessment, Assessment for Learning, self and peer assess

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30

Kumar, Alok, and Mayank Jain. Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases. Apress, 2020.

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31

López, César Pérez. DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES : ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB. Lulu Press, Inc., 2021.

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32

McPherson, Gary E., ed. The Oxford Handbook of Music Performance, Volume 2. Oxford University Press, 2022. http://dx.doi.org/10.1093/oxfordhb/9780190058869.001.0001.

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Volume 2 of the Oxford Handbook of Music Performance is designed around four distinct parts: Enhancements, Health and Wellbeing, Science, and Innovations. Chapters on the popular Feldenkrais method and Alexander technique open the volume, and these lead to chapters on peak performance and mindfulness, stage behavior, impression management and charisma, enhancing music performance appraisal, and how to build a career and the skills and competencies needed to be successful. The part dealing with health and wellbeing surveys the brain mechanisms involved in music learning and performing and music

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33

McPherson, Gary E., ed. The Oxford Handbook of Music Performance, Volume 2. Oxford University Press, 2022. http://dx.doi.org/10.1093/oxfordhb/9780190058869.001.0001.

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Volume 2 of the Oxford Handbook of Music Performance is designed around four distinct parts: Enhancements, Health and Wellbeing, Science, and Innovations. Chapters on the popular Feldenkrais method and Alexander technique open the volume, and these lead to chapters on peak performance and mindfulness, stage behavior, impression management and charisma, enhancing music performance appraisal, and how to build a career and the skills and competencies needed to be successful. The part dealing with health and wellbeing surveys the brain mechanisms involved in music learning and performing and music

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34

Coveney, Peter V., and Shunzhou Wan. Molecular Dynamics: Probability and Uncertainty. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780198893479.001.0001.

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Abstract This book explores the intersection of molecular dynamics (MD) simulation with advanced probabilistic methodologies to address the inherent uncertainties in the approach. Beginning with a clear and comprehensible introduction to classical mechanics, the book transitions into the probabilistic formulation of MD, highlighting the importance of ergodic theory, kinetic theory, and unstable periodic orbits, concepts which are largely unknown to current practitioners within the domain. It discussed ensemble-based simulations, free energy calculations and the study of polymer nanocomposites

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