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You searched for subject:(ensemble learning). Showing records 1 – 30 of 191 total matches.

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University of Ottawa

1. Alabdulrahman, Rabaa. A Comparative Study of Ensemble Active Learning .

Degree: 2014, University of Ottawa

 Data Stream mining is an important emerging topic in the data mining and machine learning domain. In a Data Stream setting, the data arrive continuously… (more)

Subjects/Keywords: Data Streams; Ensemble Learning; Active Learning; Active Ensemble Learning

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APA (6th Edition):

Alabdulrahman, R. (2014). A Comparative Study of Ensemble Active Learning . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/31805

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alabdulrahman, Rabaa. “A Comparative Study of Ensemble Active Learning .” 2014. Thesis, University of Ottawa. Accessed February 25, 2020. http://hdl.handle.net/10393/31805.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alabdulrahman, Rabaa. “A Comparative Study of Ensemble Active Learning .” 2014. Web. 25 Feb 2020.

Vancouver:

Alabdulrahman R. A Comparative Study of Ensemble Active Learning . [Internet] [Thesis]. University of Ottawa; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10393/31805.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alabdulrahman R. A Comparative Study of Ensemble Active Learning . [Thesis]. University of Ottawa; 2014. Available from: http://hdl.handle.net/10393/31805

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia State University

2. Zhu, Yun. Dynamic Ensemble Selection with Regional Expertise.

Degree: PhD, Computer Science, 2015, Georgia State University

  Many recent works have shown that ensemble methods yield better generalizability over single classifier approach by aggregating the decisions of all base learners in… (more)

Subjects/Keywords: Ensemble; MCS; DES; Metric Learning

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APA (6th Edition):

Zhu, Y. (2015). Dynamic Ensemble Selection with Regional Expertise. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/97

Chicago Manual of Style (16th Edition):

Zhu, Yun. “Dynamic Ensemble Selection with Regional Expertise.” 2015. Doctoral Dissertation, Georgia State University. Accessed February 25, 2020. https://scholarworks.gsu.edu/cs_diss/97.

MLA Handbook (7th Edition):

Zhu, Yun. “Dynamic Ensemble Selection with Regional Expertise.” 2015. Web. 25 Feb 2020.

Vancouver:

Zhu Y. Dynamic Ensemble Selection with Regional Expertise. [Internet] [Doctoral dissertation]. Georgia State University; 2015. [cited 2020 Feb 25]. Available from: https://scholarworks.gsu.edu/cs_diss/97.

Council of Science Editors:

Zhu Y. Dynamic Ensemble Selection with Regional Expertise. [Doctoral Dissertation]. Georgia State University; 2015. Available from: https://scholarworks.gsu.edu/cs_diss/97


University of Connecticut

3. Yankee, Tara N. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.

Degree: M. Eng., Biomedical Engineering, 2017, University of Connecticut

  The ability to collect and store large amounts of data is transforming data-driven discovery; recent technological advances in biology allow systematic data production and… (more)

Subjects/Keywords: clustering; ensemble learning; feature selection; unsupervised learning

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APA (6th Edition):

Yankee, T. N. (2017). Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/1123

Chicago Manual of Style (16th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed February 25, 2020. https://opencommons.uconn.edu/gs_theses/1123.

MLA Handbook (7th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Web. 25 Feb 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Feb 25]. Available from: https://opencommons.uconn.edu/gs_theses/1123.

Council of Science Editors:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Masters Thesis]. University of Connecticut; 2017. Available from: https://opencommons.uconn.edu/gs_theses/1123


Georgia Tech

4. Cao, Yang. MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST.

Degree: PhD, Building Construction, 2019, Georgia Tech

 The U.S. government attaches great importance to highway construction every year. Because of the importance of highway construction projects and the tremendous expenditure, the budgeting… (more)

Subjects/Keywords: Deep Learning; Ensemble Learning; Highway Construction Cost

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APA (6th Edition):

Cao, Y. (2019). MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61237

Chicago Manual of Style (16th Edition):

Cao, Yang. “MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST.” 2019. Doctoral Dissertation, Georgia Tech. Accessed February 25, 2020. http://hdl.handle.net/1853/61237.

MLA Handbook (7th Edition):

Cao, Yang. “MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST.” 2019. Web. 25 Feb 2020.

Vancouver:

Cao Y. MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/1853/61237.

Council of Science Editors:

Cao Y. MODELING AND PREDICTING THE VARIATION OF US HIGHWAY CONSTRUCTION COST. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61237


Louisiana State University

5. An, Xing. Ensemble Methods for Malware Diagnosis Based on One-class SVMs.

Degree: MS, Computer Sciences, 2012, Louisiana State University

 Malware diagnosis is one of today’s most popular topics of machine learning. Instead of simply applying all the classical classification algorithms to the problem and… (more)

Subjects/Keywords: rescaling; ensemble methods; machine learning; malware diagnosis

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APA (6th Edition):

An, X. (2012). Ensemble Methods for Malware Diagnosis Based on One-class SVMs. (Masters Thesis). Louisiana State University. Retrieved from etd-04102013-094841 ; https://digitalcommons.lsu.edu/gradschool_theses/2294

Chicago Manual of Style (16th Edition):

An, Xing. “Ensemble Methods for Malware Diagnosis Based on One-class SVMs.” 2012. Masters Thesis, Louisiana State University. Accessed February 25, 2020. etd-04102013-094841 ; https://digitalcommons.lsu.edu/gradschool_theses/2294.

MLA Handbook (7th Edition):

An, Xing. “Ensemble Methods for Malware Diagnosis Based on One-class SVMs.” 2012. Web. 25 Feb 2020.

Vancouver:

An X. Ensemble Methods for Malware Diagnosis Based on One-class SVMs. [Internet] [Masters thesis]. Louisiana State University; 2012. [cited 2020 Feb 25]. Available from: etd-04102013-094841 ; https://digitalcommons.lsu.edu/gradschool_theses/2294.

Council of Science Editors:

An X. Ensemble Methods for Malware Diagnosis Based on One-class SVMs. [Masters Thesis]. Louisiana State University; 2012. Available from: etd-04102013-094841 ; https://digitalcommons.lsu.edu/gradschool_theses/2294


University of Sydney

6. Chen, Hao. Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints .

Degree: 2014, University of Sydney

 In my study, Staking Multivariate Linear Regression (SMLR) is proposed for developing predictive models with better generalisation to predict the bioactivity capacity of herbal medicines… (more)

Subjects/Keywords: Chromatographic fingerprint; Bioactivity prediction; Ensemble learning

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APA (6th Edition):

Chen, H. (2014). Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/11954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Hao. “Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints .” 2014. Thesis, University of Sydney. Accessed February 25, 2020. http://hdl.handle.net/2123/11954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Hao. “Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints .” 2014. Web. 25 Feb 2020.

Vancouver:

Chen H. Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints . [Internet] [Thesis]. University of Sydney; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2123/11954.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen H. Esemble learning for prediction of the bioactivity capacity of herbal medicines from chromatographic fingerprints . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/11954

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

7. Traganitis, Panagiotis. Scalable and Ensemble Learning for Big Data.

Degree: PhD, Electrical/Computer Engineering, 2019, University of Minnesota

 The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices… (more)

Subjects/Keywords: Big Data; clustering; Ensemble; learning; subspace; unsupervised

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APA (6th Edition):

Traganitis, P. (2019). Scalable and Ensemble Learning for Big Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206358

Chicago Manual of Style (16th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Doctoral Dissertation, University of Minnesota. Accessed February 25, 2020. http://hdl.handle.net/11299/206358.

MLA Handbook (7th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Web. 25 Feb 2020.

Vancouver:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/11299/206358.

Council of Science Editors:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206358


Rochester Institute of Technology

8. Lobato Ramos, Andre. Evolutionary Weights for Random Subspace Learning.

Degree: MS, School of Mathematical Sciences (COS), 2016, Rochester Institute of Technology

Ensemble learning is a widely used technique in Data Mining, this method allows us to aggregate models to reduce prediction error. There are many… (more)

Subjects/Keywords: Ensemble learning; Machine learning; Random subspace learning; Weighting scheme

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APA (6th Edition):

Lobato Ramos, A. (2016). Evolutionary Weights for Random Subspace Learning. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9014

Chicago Manual of Style (16th Edition):

Lobato Ramos, Andre. “Evolutionary Weights for Random Subspace Learning.” 2016. Masters Thesis, Rochester Institute of Technology. Accessed February 25, 2020. https://scholarworks.rit.edu/theses/9014.

MLA Handbook (7th Edition):

Lobato Ramos, Andre. “Evolutionary Weights for Random Subspace Learning.” 2016. Web. 25 Feb 2020.

Vancouver:

Lobato Ramos A. Evolutionary Weights for Random Subspace Learning. [Internet] [Masters thesis]. Rochester Institute of Technology; 2016. [cited 2020 Feb 25]. Available from: https://scholarworks.rit.edu/theses/9014.

Council of Science Editors:

Lobato Ramos A. Evolutionary Weights for Random Subspace Learning. [Masters Thesis]. Rochester Institute of Technology; 2016. Available from: https://scholarworks.rit.edu/theses/9014


Colorado State University

9. Elliott, Daniel L. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.

Degree: PhD, Computer Science, 2018, Colorado State University

 Reinforcement learning agents learn by exploring the environment and then exploiting what they have learned. This frees the human trainers from having to know the… (more)

Subjects/Keywords: machine learning; Q-learning; ensemble; reinforcement learning; neural networks

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APA (6th Edition):

Elliott, D. L. (2018). Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/191477

Chicago Manual of Style (16th Edition):

Elliott, Daniel L. “Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Doctoral Dissertation, Colorado State University. Accessed February 25, 2020. http://hdl.handle.net/10217/191477.

MLA Handbook (7th Edition):

Elliott, Daniel L. “Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The.” 2018. Web. 25 Feb 2020.

Vancouver:

Elliott DL. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. [Internet] [Doctoral dissertation]. Colorado State University; 2018. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10217/191477.

Council of Science Editors:

Elliott DL. Wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions, The. [Doctoral Dissertation]. Colorado State University; 2018. Available from: http://hdl.handle.net/10217/191477

10. Narassiguin, Anil. Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection.

Degree: Docteur es, Informatique, 2018, Lyon

Les méthodes ensemblistes constituent un sujet de recherche très populaire au cours de la dernière décennie. Leur succès découle en grande partie de leurs solutions… (more)

Subjects/Keywords: Apprentissage ensembliste; Sélection dynamique; Multi-label; Ensemble learning; Dynamic ensemble selection; Multi-label; 004

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APA (6th Edition):

Narassiguin, A. (2018). Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2018LYSE1075

Chicago Manual of Style (16th Edition):

Narassiguin, Anil. “Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection.” 2018. Doctoral Dissertation, Lyon. Accessed February 25, 2020. http://www.theses.fr/2018LYSE1075.

MLA Handbook (7th Edition):

Narassiguin, Anil. “Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection.” 2018. Web. 25 Feb 2020.

Vancouver:

Narassiguin A. Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection. [Internet] [Doctoral dissertation]. Lyon; 2018. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2018LYSE1075.

Council of Science Editors:

Narassiguin A. Apprentissage Ensembliste, Étude comparative et Améliorations via Sélection Dynamique : Ensemble Learning, Comparative Analysis and Further Improvements with Dynamic Ensemble Selection. [Doctoral Dissertation]. Lyon; 2018. Available from: http://www.theses.fr/2018LYSE1075


University of Georgia

11. Mahamuda, Vasim. Analyzing the performance of machine learning algorithms on metagenomic data.

Degree: MS, Computer Science, 2010, University of Georgia

 Metagenomics is a branch of bioinformatics that deals with the study and analysis of micro-organisms in natural environments. Some micro-organisms including many species of bacteria,… (more)

Subjects/Keywords: Binning; decision trees; machine learning; metagenomics; ensemble methods; supervised learning.

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APA (6th Edition):

Mahamuda, V. (2010). Analyzing the performance of machine learning algorithms on metagenomic data. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms

Chicago Manual of Style (16th Edition):

Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Masters Thesis, University of Georgia. Accessed February 25, 2020. http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.

MLA Handbook (7th Edition):

Mahamuda, Vasim. “Analyzing the performance of machine learning algorithms on metagenomic data.” 2010. Web. 25 Feb 2020.

Vancouver:

Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Internet] [Masters thesis]. University of Georgia; 2010. [cited 2020 Feb 25]. Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms.

Council of Science Editors:

Mahamuda V. Analyzing the performance of machine learning algorithms on metagenomic data. [Masters Thesis]. University of Georgia; 2010. Available from: http://purl.galileo.usg.edu/uga_etd/mahamuda_vasim_201008_ms


University of Manchester

12. Zanda, Manuela. A probabilistic perspective on ensemble diversity.

Degree: PhD, 2010, University of Manchester

 We study diversity in classifier ensembles from a broader perspectivethan the 0/1 loss function, the main reason being that the bias-variance decomposition of the 0/1… (more)

Subjects/Keywords: 006.3; Ensemble Learning; classifier diversity; Machine Learning; Information Theory

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APA (6th Edition):

Zanda, M. (2010). A probabilistic perspective on ensemble diversity. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/a-probabilistic-perspective-on-ensemble-diversity(06296f74-806a-42dc-a65f-f7607f67d9f5).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525926

Chicago Manual of Style (16th Edition):

Zanda, Manuela. “A probabilistic perspective on ensemble diversity.” 2010. Doctoral Dissertation, University of Manchester. Accessed February 25, 2020. https://www.research.manchester.ac.uk/portal/en/theses/a-probabilistic-perspective-on-ensemble-diversity(06296f74-806a-42dc-a65f-f7607f67d9f5).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525926.

MLA Handbook (7th Edition):

Zanda, Manuela. “A probabilistic perspective on ensemble diversity.” 2010. Web. 25 Feb 2020.

Vancouver:

Zanda M. A probabilistic perspective on ensemble diversity. [Internet] [Doctoral dissertation]. University of Manchester; 2010. [cited 2020 Feb 25]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/a-probabilistic-perspective-on-ensemble-diversity(06296f74-806a-42dc-a65f-f7607f67d9f5).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525926.

Council of Science Editors:

Zanda M. A probabilistic perspective on ensemble diversity. [Doctoral Dissertation]. University of Manchester; 2010. Available from: https://www.research.manchester.ac.uk/portal/en/theses/a-probabilistic-perspective-on-ensemble-diversity(06296f74-806a-42dc-a65f-f7607f67d9f5).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525926


University of Manchester

13. Zanda, Manuela. A Probabilistic Perspective on Ensemble Diversity.

Degree: 2010, University of Manchester

 We study diversity in classifier ensembles from a broader perspectivethan the 0/1 loss function, the main reason being that thebias-variance decomposition of the 0/1 loss… (more)

Subjects/Keywords: Ensemble Learning; classifier diversity; Machine Learning; Information Theory

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APA (6th Edition):

Zanda, M. (2010). A Probabilistic Perspective on Ensemble Diversity. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566

Chicago Manual of Style (16th Edition):

Zanda, Manuela. “A Probabilistic Perspective on Ensemble Diversity.” 2010. Doctoral Dissertation, University of Manchester. Accessed February 25, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566.

MLA Handbook (7th Edition):

Zanda, Manuela. “A Probabilistic Perspective on Ensemble Diversity.” 2010. Web. 25 Feb 2020.

Vancouver:

Zanda M. A Probabilistic Perspective on Ensemble Diversity. [Internet] [Doctoral dissertation]. University of Manchester; 2010. [cited 2020 Feb 25]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566.

Council of Science Editors:

Zanda M. A Probabilistic Perspective on Ensemble Diversity. [Doctoral Dissertation]. University of Manchester; 2010. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:94566


NSYSU

14. Lin, Chia-Hung. Study on MIMO Antenna Selection Based on Deep Learning.

Degree: Master, Communications Engineering, 2018, NSYSU

 MIMO technology can improve the spectral efficiency of the communication system obviously. And we usually employ antenna selection technology when we implement MIMO on the… (more)

Subjects/Keywords: deep learning; antenna selection; MIMO; overfitting; ensemble learning

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APA (6th Edition):

Lin, C. (2018). Study on MIMO Antenna Selection Based on Deep Learning. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0624118-111521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lin, Chia-Hung. “Study on MIMO Antenna Selection Based on Deep Learning.” 2018. Thesis, NSYSU. Accessed February 25, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0624118-111521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lin, Chia-Hung. “Study on MIMO Antenna Selection Based on Deep Learning.” 2018. Web. 25 Feb 2020.

Vancouver:

Lin C. Study on MIMO Antenna Selection Based on Deep Learning. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0624118-111521.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin C. Study on MIMO Antenna Selection Based on Deep Learning. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0624118-111521

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Vanderbilt University

15. Castellanos, Jason Alfred. Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning.

Degree: MS, Biomedical Informatics, 2016, Vanderbilt University

 Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality in the United States. A key therapeutic dilemma in the treatment of CRC is whether… (more)

Subjects/Keywords: Colorectal Cancer; Machine Learning; Ensemble Learning; Molecular Risk Stratification

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APA (6th Edition):

Castellanos, J. A. (2016). Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning. (Masters Thesis). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu/available/etd-06142016-101117/ ;

Chicago Manual of Style (16th Edition):

Castellanos, Jason Alfred. “Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning.” 2016. Masters Thesis, Vanderbilt University. Accessed February 25, 2020. http://etd.library.vanderbilt.edu/available/etd-06142016-101117/ ;.

MLA Handbook (7th Edition):

Castellanos, Jason Alfred. “Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning.” 2016. Web. 25 Feb 2020.

Vancouver:

Castellanos JA. Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning. [Internet] [Masters thesis]. Vanderbilt University; 2016. [cited 2020 Feb 25]. Available from: http://etd.library.vanderbilt.edu/available/etd-06142016-101117/ ;.

Council of Science Editors:

Castellanos JA. Predicting Colorectal Cancer Recurrence by Utilizing Multiple-View Multiple-Learner Supervised Learning. [Masters Thesis]. Vanderbilt University; 2016. Available from: http://etd.library.vanderbilt.edu/available/etd-06142016-101117/ ;


University of Ottawa

16. Floyd, Sean Louis Alan. Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams .

Degree: 2019, University of Ottawa

 In this thesis, learning refers to the intelligent computational extraction of knowledge from data. Supervised learning tasks require data to be annotated with labels, whereas… (more)

Subjects/Keywords: Ensemble learning; Data streams; Concept drift; Online learning; Non-stationary environments

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Floyd, S. L. A. (2019). Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/39273

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Floyd, Sean Louis Alan. “Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams .” 2019. Thesis, University of Ottawa. Accessed February 25, 2020. http://hdl.handle.net/10393/39273.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Floyd, Sean Louis Alan. “Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams .” 2019. Web. 25 Feb 2020.

Vancouver:

Floyd SLA. Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams . [Internet] [Thesis]. University of Ottawa; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10393/39273.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Floyd SLA. Semi-Supervised Hybrid Windowing Ensembles for Learning from Evolving Streams . [Thesis]. University of Ottawa; 2019. Available from: http://hdl.handle.net/10393/39273

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Technology, Sydney

17. Liang, G. Ensemble predictions : empirical studies on learners' performance and sample distributions.

Degree: 2014, University of Technology, Sydney

 Imbalanced data problems are among the most challenging in Data Mining and Machine Learning research. This dissertation investigates the performance of ensemble learning systems on… (more)

Subjects/Keywords: Ensemble learning.; Imbalanced class distribution.; Sampling techniques.; Machine learning.; Data mining.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liang, G. (2014). Ensemble predictions : empirical studies on learners' performance and sample distributions. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/28019

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liang, G. “Ensemble predictions : empirical studies on learners' performance and sample distributions.” 2014. Thesis, University of Technology, Sydney. Accessed February 25, 2020. http://hdl.handle.net/10453/28019.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Liang, G. “Ensemble predictions : empirical studies on learners' performance and sample distributions.” 2014. Web. 25 Feb 2020.

Vancouver:

Liang G. Ensemble predictions : empirical studies on learners' performance and sample distributions. [Internet] [Thesis]. University of Technology, Sydney; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10453/28019.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liang G. Ensemble predictions : empirical studies on learners' performance and sample distributions. [Thesis]. University of Technology, Sydney; 2014. Available from: http://hdl.handle.net/10453/28019

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Victoria University of Wellington

18. Evans, Benjamin. Population-based Ensemble Learning with Tree Structures for Classification.

Degree: 2019, Victoria University of Wellington

Ensemble learning is one of the most powerful extensions for improving upon individual machine learning models. Rather than a single model being used, several models… (more)

Subjects/Keywords: Machine learning; Ensemble learning; Interpretable machine learning; Genetic programming; Automated machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Evans, B. (2019). Population-based Ensemble Learning with Tree Structures for Classification. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/8179

Chicago Manual of Style (16th Edition):

Evans, Benjamin. “Population-based Ensemble Learning with Tree Structures for Classification.” 2019. Masters Thesis, Victoria University of Wellington. Accessed February 25, 2020. http://hdl.handle.net/10063/8179.

MLA Handbook (7th Edition):

Evans, Benjamin. “Population-based Ensemble Learning with Tree Structures for Classification.” 2019. Web. 25 Feb 2020.

Vancouver:

Evans B. Population-based Ensemble Learning with Tree Structures for Classification. [Internet] [Masters thesis]. Victoria University of Wellington; 2019. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10063/8179.

Council of Science Editors:

Evans B. Population-based Ensemble Learning with Tree Structures for Classification. [Masters Thesis]. Victoria University of Wellington; 2019. Available from: http://hdl.handle.net/10063/8179

19. Ferreira, Ednaldo José. Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos.

Degree: PhD, Ciências de Computação e Matemática Computacional, 2012, University of São Paulo

O desenvolvimento de novas técnicas capazes de produzir modelos de predição com erros de generalização relativamente baixos é uma constante em aprendizado de máquina e… (more)

Subjects/Keywords: Aprendizado de Ensemble; Aprendizado de máquina; Ensemble baseado em roto-projeção otimizada; Ensemble learning; Ensemble method; Machine learning; Método de ensemble; Optimized roto-projection; Optimized roto-projection ensemble; Roto-projeção otimizada

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ferreira, E. J. (2012). Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27062012-161603/ ;

Chicago Manual of Style (16th Edition):

Ferreira, Ednaldo José. “Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos.” 2012. Doctoral Dissertation, University of São Paulo. Accessed February 25, 2020. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27062012-161603/ ;.

MLA Handbook (7th Edition):

Ferreira, Ednaldo José. “Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos.” 2012. Web. 25 Feb 2020.

Vancouver:

Ferreira EJ. Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos. [Internet] [Doctoral dissertation]. University of São Paulo; 2012. [cited 2020 Feb 25]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27062012-161603/ ;.

Council of Science Editors:

Ferreira EJ. Método baseado em rotação e projeção otimizadas para a construção de ensembles de modelos. [Doctoral Dissertation]. University of São Paulo; 2012. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-27062012-161603/ ;


University of Minnesota

20. Karpatne, Anuj. Predictive Learning with Heterogeneity in Populations.

Degree: PhD, Computer Science, 2017, University of Minnesota

 Predictive learning forms the backbone of several data-driven systems powering scientific as well as commercial applications, e.g., filtering spam messages, detecting faces in images, forecasting… (more)

Subjects/Keywords: data mining; ensemble learning; machine learning; multi-modality; multi-task learning; population heterogeneity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Karpatne, A. (2017). Predictive Learning with Heterogeneity in Populations. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/192667

Chicago Manual of Style (16th Edition):

Karpatne, Anuj. “Predictive Learning with Heterogeneity in Populations.” 2017. Doctoral Dissertation, University of Minnesota. Accessed February 25, 2020. http://hdl.handle.net/11299/192667.

MLA Handbook (7th Edition):

Karpatne, Anuj. “Predictive Learning with Heterogeneity in Populations.” 2017. Web. 25 Feb 2020.

Vancouver:

Karpatne A. Predictive Learning with Heterogeneity in Populations. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/11299/192667.

Council of Science Editors:

Karpatne A. Predictive Learning with Heterogeneity in Populations. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/192667


University of Southern California

21. Liu, Tsung-Jung. A learning‐based approach to image quality assessment.

Degree: PhD, Electrical Engineering, 2016, University of Southern California

 Research on visual quality assessment has been active during the last decade. This dissertation consists of six parts centered on this subject. In Chapter 1,… (more)

Subjects/Keywords: ensemble; fusion; image quality assessment; image quality scorer; machine learning; ParaBoost

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, T. (2016). A learning‐based approach to image quality assessment. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/446378/rec/226

Chicago Manual of Style (16th Edition):

Liu, Tsung-Jung. “A learning‐based approach to image quality assessment.” 2016. Doctoral Dissertation, University of Southern California. Accessed February 25, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/446378/rec/226.

MLA Handbook (7th Edition):

Liu, Tsung-Jung. “A learning‐based approach to image quality assessment.” 2016. Web. 25 Feb 2020.

Vancouver:

Liu T. A learning‐based approach to image quality assessment. [Internet] [Doctoral dissertation]. University of Southern California; 2016. [cited 2020 Feb 25]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/446378/rec/226.

Council of Science Editors:

Liu T. A learning‐based approach to image quality assessment. [Doctoral Dissertation]. University of Southern California; 2016. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/446378/rec/226


The Ohio State University

22. King, Tyler C. Factors Influencing Adults' Participation in Community Bands of Central Ohio.

Degree: MA, Music, 2009, The Ohio State University

  The purpose of this study was to create a profile of adult community band members in Central Ohio. Specifically, this study sought to describe… (more)

Subjects/Keywords: Music; Music Education; community bands; community ensemble; adult learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

King, T. C. (2009). Factors Influencing Adults' Participation in Community Bands of Central Ohio. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1243881978

Chicago Manual of Style (16th Edition):

King, Tyler C. “Factors Influencing Adults' Participation in Community Bands of Central Ohio.” 2009. Masters Thesis, The Ohio State University. Accessed February 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243881978.

MLA Handbook (7th Edition):

King, Tyler C. “Factors Influencing Adults' Participation in Community Bands of Central Ohio.” 2009. Web. 25 Feb 2020.

Vancouver:

King TC. Factors Influencing Adults' Participation in Community Bands of Central Ohio. [Internet] [Masters thesis]. The Ohio State University; 2009. [cited 2020 Feb 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1243881978.

Council of Science Editors:

King TC. Factors Influencing Adults' Participation in Community Bands of Central Ohio. [Masters Thesis]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1243881978


University of Southern California

23. Audhkhasi, Kartik. A computational framework for diversity in ensembles of humans and machine systems.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 My Ph.D. thesis presents a computational framework for diversity in ensembles or collections of humans and machine systems used for signal and information processing. Machine… (more)

Subjects/Keywords: signal processing; information processing; machine learning; ensemble methods; diversity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Audhkhasi, K. (2014). A computational framework for diversity in ensembles of humans and machine systems. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144

Chicago Manual of Style (16th Edition):

Audhkhasi, Kartik. “A computational framework for diversity in ensembles of humans and machine systems.” 2014. Doctoral Dissertation, University of Southern California. Accessed February 25, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144.

MLA Handbook (7th Edition):

Audhkhasi, Kartik. “A computational framework for diversity in ensembles of humans and machine systems.” 2014. Web. 25 Feb 2020.

Vancouver:

Audhkhasi K. A computational framework for diversity in ensembles of humans and machine systems. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2020 Feb 25]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144.

Council of Science Editors:

Audhkhasi K. A computational framework for diversity in ensembles of humans and machine systems. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144


University of Sydney

24. Wong, Wai Ho. Predicting Demand in Cloud Computing Environments .

Degree: 2013, University of Sydney

 Cloud computing is a new computing paradigm that enables elastic on-demand pay-per-use access to shared computational resources. However, there are current limitations on the elasticity… (more)

Subjects/Keywords: time-series; forecasting; prediction; cloud computing; boosting; ensemble learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wong, W. H. (2013). Predicting Demand in Cloud Computing Environments . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/9497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wong, Wai Ho. “Predicting Demand in Cloud Computing Environments .” 2013. Thesis, University of Sydney. Accessed February 25, 2020. http://hdl.handle.net/2123/9497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wong, Wai Ho. “Predicting Demand in Cloud Computing Environments .” 2013. Web. 25 Feb 2020.

Vancouver:

Wong WH. Predicting Demand in Cloud Computing Environments . [Internet] [Thesis]. University of Sydney; 2013. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2123/9497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wong WH. Predicting Demand in Cloud Computing Environments . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/9497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of South Florida

25. Shaikh, Farooq Israr Ahmed. Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning.

Degree: 2019, University of South Florida

 The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet… (more)

Subjects/Keywords: Darknet; Deep Learning; Ensemble Learners; Network Statistics; Electrical and Computer Engineering

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APA (6th Edition):

Shaikh, F. I. A. (2019). Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning. (Thesis). University of South Florida. Retrieved from https://scholarcommons.usf.edu/etd/7935

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shaikh, Farooq Israr Ahmed. “Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning.” 2019. Thesis, University of South Florida. Accessed February 25, 2020. https://scholarcommons.usf.edu/etd/7935.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shaikh, Farooq Israr Ahmed. “Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning.” 2019. Web. 25 Feb 2020.

Vancouver:

Shaikh FIA. Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning. [Internet] [Thesis]. University of South Florida; 2019. [cited 2020 Feb 25]. Available from: https://scholarcommons.usf.edu/etd/7935.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shaikh FIA. Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning. [Thesis]. University of South Florida; 2019. Available from: https://scholarcommons.usf.edu/etd/7935

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Harvard University

26. Liu, Jeremiah Zhe. Practical Semiparametric Inference With Bayesian Nonparametric Ensembles.

Degree: PhD, 2019, Harvard University

Set in the practical situation where the data-generating process is not known and there are multiple imperfect candidate models available, this thesis studies how to… (more)

Subjects/Keywords: Bayesian Nonparametrics; Ensemble Learning; Robustness; Hypothesis Testing; Spatio-temporal Modeling

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APA (6th Edition):

Liu, J. Z. (2019). Practical Semiparametric Inference With Bayesian Nonparametric Ensembles. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029618

Chicago Manual of Style (16th Edition):

Liu, Jeremiah Zhe. “Practical Semiparametric Inference With Bayesian Nonparametric Ensembles.” 2019. Doctoral Dissertation, Harvard University. Accessed February 25, 2020. http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029618.

MLA Handbook (7th Edition):

Liu, Jeremiah Zhe. “Practical Semiparametric Inference With Bayesian Nonparametric Ensembles.” 2019. Web. 25 Feb 2020.

Vancouver:

Liu JZ. Practical Semiparametric Inference With Bayesian Nonparametric Ensembles. [Internet] [Doctoral dissertation]. Harvard University; 2019. [cited 2020 Feb 25]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029618.

Council of Science Editors:

Liu JZ. Practical Semiparametric Inference With Bayesian Nonparametric Ensembles. [Doctoral Dissertation]. Harvard University; 2019. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:42029618


Virginia Tech

27. King, Michael Allen. Ensemble Learning Techniques for Structured and Unstructured Data.

Degree: PhD, Business Information Technology, 2015, Virginia Tech

 This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual… (more)

Subjects/Keywords: ensemble methods; data mining; machine learning; classification; structured data; unstructured data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

King, M. A. (2015). Ensemble Learning Techniques for Structured and Unstructured Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51667

Chicago Manual of Style (16th Edition):

King, Michael Allen. “Ensemble Learning Techniques for Structured and Unstructured Data.” 2015. Doctoral Dissertation, Virginia Tech. Accessed February 25, 2020. http://hdl.handle.net/10919/51667.

MLA Handbook (7th Edition):

King, Michael Allen. “Ensemble Learning Techniques for Structured and Unstructured Data.” 2015. Web. 25 Feb 2020.

Vancouver:

King MA. Ensemble Learning Techniques for Structured and Unstructured Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10919/51667.

Council of Science Editors:

King MA. Ensemble Learning Techniques for Structured and Unstructured Data. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/51667


Virginia Tech

28. Ngo, Khai Thoi. Stacking Ensemble for auto_ml.

Degree: MS, Electrical and Computer Engineering, 2018, Virginia Tech

 Machine learning has been a subject undergoing intense study across many different industries and academic research areas. Companies and researchers have taken full advantages of… (more)

Subjects/Keywords: Machine Learning; Stacking Ensemble; Model Selection; Hyper-parameter optimization; auto_ml

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APA (6th Edition):

Ngo, K. T. (2018). Stacking Ensemble for auto_ml. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83547

Chicago Manual of Style (16th Edition):

Ngo, Khai Thoi. “Stacking Ensemble for auto_ml.” 2018. Masters Thesis, Virginia Tech. Accessed February 25, 2020. http://hdl.handle.net/10919/83547.

MLA Handbook (7th Edition):

Ngo, Khai Thoi. “Stacking Ensemble for auto_ml.” 2018. Web. 25 Feb 2020.

Vancouver:

Ngo KT. Stacking Ensemble for auto_ml. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10919/83547.

Council of Science Editors:

Ngo KT. Stacking Ensemble for auto_ml. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83547


University of Cambridge

29. Malinin, Andrey. Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment.

Degree: PhD, 2019, University of Cambridge

 Since convolutional neural networks (CNNs) achieved top performance on the ImageNet task in 2012, deep learning has become the preferred approach to addressing computer vision,… (more)

Subjects/Keywords: Deep Learning; Uncertainty Estimation; Prior Networks; Spoken Language Assessment; Ensemble Approaches

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Malinin, A. (2019). Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/298857

Chicago Manual of Style (16th Edition):

Malinin, Andrey. “Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment.” 2019. Doctoral Dissertation, University of Cambridge. Accessed February 25, 2020. https://www.repository.cam.ac.uk/handle/1810/298857.

MLA Handbook (7th Edition):

Malinin, Andrey. “Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment.” 2019. Web. 25 Feb 2020.

Vancouver:

Malinin A. Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Feb 25]. Available from: https://www.repository.cam.ac.uk/handle/1810/298857.

Council of Science Editors:

Malinin A. Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/298857


NSYSU

30. Hsiao, Po-Wei. Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition.

Degree: Master, Computer Science and Engineering, 2018, NSYSU

 This study uses deep neural networks to construct the static and dynamic speech emotion recognition systems and integrates the static and dynamic models by ensemble(more)

Subjects/Keywords: attention mechanism; speech emotion recognition; ensemble learning; deep neural networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hsiao, P. (2018). Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730118-100155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hsiao, Po-Wei. “Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition.” 2018. Thesis, NSYSU. Accessed February 25, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730118-100155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hsiao, Po-Wei. “Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition.” 2018. Web. 25 Feb 2020.

Vancouver:

Hsiao P. Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 25]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730118-100155.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hsiao P. Deep Neural Networks and Ensemble Learning with Application to Speech Emotion Recognition. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730118-100155

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2] [3] [4] [5] [6] [7]

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