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Books on the topic 'Ensemble learning methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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1

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

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2

Pattern classification using ensemble methods. Singapore: World Scientific, 2010.

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3

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

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4

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

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5

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

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6

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

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7

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

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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

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

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12

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

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13

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

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14

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

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15

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

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16

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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17

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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18

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

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19

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

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20

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

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21

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

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22

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

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23

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

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24

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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25

Supervised and Unsupervised Ensemble Methods and Their Applications Studies in Computational Intelligence. Springer, 2008.

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26

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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27

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

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28

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

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29

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

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30

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 assessment, and authentic assessment is clarified and illustrated with music examples. Readers will learn practical strategies for choosing which concepts to assess, which methods to use, and how to use results to provide accurate and effective feedback to students. The second section brings assessment fundamentals into the music room. Filled with practical advice, each chapter examines a different facet of musicianship. Sample assessments in all performance areas are provided, including concert preparation, music literacy, fundamentals and technique, terminology, interpretation, evaluation and critique, composition and improvisation, beliefs and attitudes, and more. The final section is an examination of grading practices in music classes. Readers will gain information about ensemble grades that communicate what students know and are able to do. The book concludes with ways for music educators to take their first steps toward implementing these strategies in their own teaching, including the use of instructional technology. Assessing like an expert is possible, and this book is just what teachers need to get started.
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31

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 musical activities in people with disabilities, performance anxiety, diseases and health risks in instrumentalists, hearing and voice, and finally, a discussion of how to promote a healthy related lifestyle. The first six chapters of the Science part cover the basic science underlying the operation of wind, brass, string instruments, and the piano, and two chapters covering the solo voice and vocal ensembles. The final two chapters explain digital musical instruments and the practical issues that researchers and performers face when using motion capture technology to study movement during musical performances. The four chapters of the Innovations part address the types of technological and social and wellbeing innovations that are reshaping how musicians conceive their performances in the twenty-first century.
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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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Abstract:
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 musical activities in people with disabilities, performance anxiety, diseases and health risks in instrumentalists, hearing and voice, and finally, a discussion of how to promote a healthy related lifestyle. The first six chapters of the Science part cover the basic science underlying the operation of wind, brass, string instruments, and the piano, and two chapters covering the solo voice and vocal ensembles. The final two chapters explain digital musical instruments and the practical issues that researchers and performers face when using motion capture technology to study movement during musical performances. The four chapters of the Innovations part address the types of technological and social and wellbeing innovations that are reshaping how musicians conceive their performances in the twenty-first century.
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