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You searched for subject:(machine learning models). Showing records 1 – 30 of 392 total matches.

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University of Michigan

1. Kaja, Nevrus. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.

Degree: PhD, College of Engineering & Computer Science, 2019, University of Michigan

 Automotive technology has continued to advance in many aspects. As an outcome of such advancements, autonomous vehicles are closer to commercialization and have brought to… (more)

Subjects/Keywords: Artificial intelligence; Cybersecurity; Machine learning; Fuzzy logic; Threat models; Automotive; Electrical and Computer Engineering

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APA (6th Edition):

Kaja, N. (2019). Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/149467

Chicago Manual of Style (16th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Doctoral Dissertation, University of Michigan. Accessed August 18, 2019. http://hdl.handle.net/2027.42/149467.

MLA Handbook (7th Edition):

Kaja, Nevrus. “Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms.” 2019. Web. 18 Aug 2019.

Vancouver:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2027.42/149467.

Council of Science Editors:

Kaja N. Artificial Intelligence and Cybersecurity: Building an Automotive Cybersecurity Framework Using Machine Learning Algorithms. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/149467


Pontifical Catholic University of Rio de Janeiro

2. JORGE LUIZ CATALDO FALBO SANTO. [en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS.

Degree: 2019, Pontifical Catholic University of Rio de Janeiro

[pt] À medida que os modelos de aprendizado de máquina penetram áreas críticas como medicina, sistema de justiça criminal e mercados financeiros, sua opacidade, que… (more)

Subjects/Keywords: [pt] APRENDIZADO DE MAQUINA; [en] MACHINE LEARNING; [pt] INTELIGENCIA ARTIFICIAL; [en] ARTIFICIAL INTELLIGENCE; [pt] INTELIGENCIA ARTIFICIAL EXPLICAVEL; [en] EXPLAINABLE AI; [pt] INTERPRETABILIDADE DE MODELO; [en] INTERPRETABILITY OF MODELS; [pt] EXPLANABILIDADE DE MODELO; [en] EXPLAINABILITY OF MODEL; [pt] ALGORITMO INTERPRETAVEL; [en] INTERPRETABLE ALGORITHM; [pt] TRANSPARENCIA DE ALGORITIMO; [en] ALGORITHMIC TRANSPARENCY; [pt] IA - INTELIGENCIA ARTIFICIAL; [en] AI - ARTIFICIAL INTELLIGENCE

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APA (6th Edition):

SANTO, J. L. C. F. (2019). [en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=42398

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):

SANTO, JORGE LUIZ CATALDO FALBO. “[en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS.” 2019. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed August 18, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=42398.

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

MLA Handbook (7th Edition):

SANTO, JORGE LUIZ CATALDO FALBO. “[en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS.” 2019. Web. 18 Aug 2019.

Vancouver:

SANTO JLCF. [en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. [cited 2019 Aug 18]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=42398.

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

Council of Science Editors:

SANTO JLCF. [en] A CRITICAL VIEW ON THE INTERPRETABILITY OF MACHINE LEARNING MODELS. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2019. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=42398

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


California State University – Sacramento

3. Jain, Anshul. Comparative analysis of indoor localization using machine learning models.

Degree: MS, Computer Science, 2019, California State University – Sacramento

 Indoor localization has become one of the most talked about services in today???s technology. We have observed that there have been huge demand of Indoor… (more)

Subjects/Keywords: Received signal strength; Test error of machine learning models on CPU; WiFi fingerprinting method

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APA (6th Edition):

Jain, A. (2019). Comparative analysis of indoor localization using machine learning models. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/210277

Chicago Manual of Style (16th Edition):

Jain, Anshul. “Comparative analysis of indoor localization using machine learning models.” 2019. Masters Thesis, California State University – Sacramento. Accessed August 18, 2019. http://hdl.handle.net/10211.3/210277.

MLA Handbook (7th Edition):

Jain, Anshul. “Comparative analysis of indoor localization using machine learning models.” 2019. Web. 18 Aug 2019.

Vancouver:

Jain A. Comparative analysis of indoor localization using machine learning models. [Internet] [Masters thesis]. California State University – Sacramento; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10211.3/210277.

Council of Science Editors:

Jain A. Comparative analysis of indoor localization using machine learning models. [Masters Thesis]. California State University – Sacramento; 2019. Available from: http://hdl.handle.net/10211.3/210277


Chapman University

4. Gapper, Justin J. Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images.

Degree: PhD, Computational and Data Sciences, 2019, Chapman University

  This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing… (more)

Subjects/Keywords: Data Science; Machine Learning; Statistics; Applied Mathematics; Remote Sensing; Coral Reef; Environmental Indicators and Impact Assessment; Longitudinal Data Analysis and Time Series; Oceanography; Statistical Models

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APA (6th Edition):

Gapper, J. J. (2019). Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images. (Doctoral Dissertation). Chapman University. Retrieved from https://digitalcommons.chapman.edu/cads_dissertations/2

Chicago Manual of Style (16th Edition):

Gapper, Justin J. “Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images.” 2019. Doctoral Dissertation, Chapman University. Accessed August 18, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.

MLA Handbook (7th Edition):

Gapper, Justin J. “Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images.” 2019. Web. 18 Aug 2019.

Vancouver:

Gapper JJ. Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images. [Internet] [Doctoral dissertation]. Chapman University; 2019. [cited 2019 Aug 18]. Available from: https://digitalcommons.chapman.edu/cads_dissertations/2.

Council of Science Editors:

Gapper JJ. Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images. [Doctoral Dissertation]. Chapman University; 2019. Available from: https://digitalcommons.chapman.edu/cads_dissertations/2


McMaster University

5. Habboubi, Sameh. Analytics Models for Corporate Social Responsibility in Global Supply Chains.

Degree: MSc, 2019, McMaster University

There have been several infamous incidences where world-renowned corporations have been caught by surprise when a low-tier downstream supplier has been publicly found to be… (more)

Subjects/Keywords: Analytics models; corporate social responsibility; supplier selection and lot-sizing; predictive modeling; machine learning

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APA (6th Edition):

Habboubi, S. (2019). Analytics Models for Corporate Social Responsibility in Global Supply Chains. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23997

Chicago Manual of Style (16th Edition):

Habboubi, Sameh. “Analytics Models for Corporate Social Responsibility in Global Supply Chains.” 2019. Masters Thesis, McMaster University. Accessed August 18, 2019. http://hdl.handle.net/11375/23997.

MLA Handbook (7th Edition):

Habboubi, Sameh. “Analytics Models for Corporate Social Responsibility in Global Supply Chains.” 2019. Web. 18 Aug 2019.

Vancouver:

Habboubi S. Analytics Models for Corporate Social Responsibility in Global Supply Chains. [Internet] [Masters thesis]. McMaster University; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/11375/23997.

Council of Science Editors:

Habboubi S. Analytics Models for Corporate Social Responsibility in Global Supply Chains. [Masters Thesis]. McMaster University; 2019. Available from: http://hdl.handle.net/11375/23997


Stellenbosch University

6. Briers, C. J. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.

Degree: MSc, Mathematical Sciences, 2019, Stellenbosch University

ENGLISH ABSTRACT : The Vanderkloof Dam, located on the Orange River, is responsible for the water supply to consumers along its 1 400 km reach… (more)

Subjects/Keywords: Streamflow  – South Africa; Machine learning; Deep learning; UCTD; Rivers  – Mathematical models; Orange River  – South Africa; Vanderkloof Dam  – South Africa; Vaal River  – South Africa

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APA (6th Edition):

Briers, C. J. (2019). Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105754

Chicago Manual of Style (16th Edition):

Briers, C J. “Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.” 2019. Masters Thesis, Stellenbosch University. Accessed August 18, 2019. http://hdl.handle.net/10019.1/105754.

MLA Handbook (7th Edition):

Briers, C J. “Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa.” 2019. Web. 18 Aug 2019.

Vancouver:

Briers CJ. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. [Internet] [Masters thesis]. Stellenbosch University; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10019.1/105754.

Council of Science Editors:

Briers CJ. Data-driven river flow routing using deep learning: predicting flow along the lower Orange river, Southern Africa. [Masters Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105754


Stellenbosch University

7. Kingwill, Russell. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.

Degree: MSc, Mathematical Sciences, 2019, Stellenbosch University

ENGLISH ABSTRACT : South Africa has a number of fuel grades available to consumers, one of the most popular being the 95 unleaded standard. The… (more)

Subjects/Keywords: Fuel prices  – Forecasting; Machine learning; Neural networks; UCTD; Brent crude oil  – Prices; Fuel prices  – Mathematical models

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APA (6th Edition):

Kingwill, R. (2019). Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. (Masters Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/105838

Chicago Manual of Style (16th Edition):

Kingwill, Russell. “Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.” 2019. Masters Thesis, Stellenbosch University. Accessed August 18, 2019. http://hdl.handle.net/10019.1/105838.

MLA Handbook (7th Edition):

Kingwill, Russell. “Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices.” 2019. Web. 18 Aug 2019.

Vancouver:

Kingwill R. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. [Internet] [Masters thesis]. Stellenbosch University; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10019.1/105838.

Council of Science Editors:

Kingwill R. Evaluating the effectiveness of neural network techniques in the forecasting of South African basic fuel prices. [Masters Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/105838


Georgia Tech

8. Choi, Sun. A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As… (more)

Subjects/Keywords: Air traffic; Airport capacity; Flight delays; Artificial neural networks; Machine learning; Predictive models

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APA (6th Edition):

Choi, S. (2019). A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61196

Chicago Manual of Style (16th Edition):

Choi, Sun. “A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow.” 2019. Doctoral Dissertation, Georgia Tech. Accessed August 18, 2019. http://hdl.handle.net/1853/61196.

MLA Handbook (7th Edition):

Choi, Sun. “A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow.” 2019. Web. 18 Aug 2019.

Vancouver:

Choi S. A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1853/61196.

Council of Science Editors:

Choi S. A Multi-Level Predictive Methodology for Terminal Area Air Traffic Flow. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61196


Georgia Southern University

9. Lewis, Rachel A. Data Patterns Discovery Using Unsupervised Learning.

Degree: MSin Mathematics (M.S.), Department of Mathematical Sciences, 2019, Georgia Southern University

  Self-care activities classification poses significant challenges in identifying children’s unique functional abilities and needs within the exceptional children healthcare system. The accuracy of diagnosing… (more)

Subjects/Keywords: clustering; machine learning; diagnosis; feature selection; self care problems; resampling; Applied Statistics; Artificial Intelligence and Robotics; Multivariate Analysis; Occupational Therapy; Pediatrics; Statistical Models

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APA (6th Edition):

Lewis, R. A. (2019). Data Patterns Discovery Using Unsupervised Learning. (Masters Thesis). Georgia Southern University. Retrieved from https://digitalcommons.georgiasouthern.edu/etd/1934

Chicago Manual of Style (16th Edition):

Lewis, Rachel A. “Data Patterns Discovery Using Unsupervised Learning.” 2019. Masters Thesis, Georgia Southern University. Accessed August 18, 2019. https://digitalcommons.georgiasouthern.edu/etd/1934.

MLA Handbook (7th Edition):

Lewis, Rachel A. “Data Patterns Discovery Using Unsupervised Learning.” 2019. Web. 18 Aug 2019.

Vancouver:

Lewis RA. Data Patterns Discovery Using Unsupervised Learning. [Internet] [Masters thesis]. Georgia Southern University; 2019. [cited 2019 Aug 18]. Available from: https://digitalcommons.georgiasouthern.edu/etd/1934.

Council of Science Editors:

Lewis RA. Data Patterns Discovery Using Unsupervised Learning. [Masters Thesis]. Georgia Southern University; 2019. Available from: https://digitalcommons.georgiasouthern.edu/etd/1934


Edith Cowan University

10. Peacock, Matthew. Anomaly Detection in BACnet/IP managed Building Automation Systems.

Degree: 2019, Edith Cowan University

 Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be… (more)

Subjects/Keywords: intrustion detection; Network security; Machine learning Hidden Markov models; Artificial Intelligence and Robotics; Information Security

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APA (6th Edition):

Peacock, M. (2019). Anomaly Detection in BACnet/IP managed Building Automation Systems. (Thesis). Edith Cowan University. Retrieved from https://ro.ecu.edu.au/theses/2178

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):

Peacock, Matthew. “Anomaly Detection in BACnet/IP managed Building Automation Systems.” 2019. Thesis, Edith Cowan University. Accessed August 18, 2019. https://ro.ecu.edu.au/theses/2178.

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

MLA Handbook (7th Edition):

Peacock, Matthew. “Anomaly Detection in BACnet/IP managed Building Automation Systems.” 2019. Web. 18 Aug 2019.

Vancouver:

Peacock M. Anomaly Detection in BACnet/IP managed Building Automation Systems. [Internet] [Thesis]. Edith Cowan University; 2019. [cited 2019 Aug 18]. Available from: https://ro.ecu.edu.au/theses/2178.

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

Council of Science Editors:

Peacock M. Anomaly Detection in BACnet/IP managed Building Automation Systems. [Thesis]. Edith Cowan University; 2019. Available from: https://ro.ecu.edu.au/theses/2178

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


University of California – San Diego

11. Amatya, Debha Narsingh. Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches.

Degree: Neurosciences, 2019, University of California – San Diego

 Autism spectrum disorder (ASD) is a neurodevelopmental condition that heterogeneously impacts core domains of human function, such as communication, socialization, and cognition. Underlying these symptoms… (more)

Subjects/Keywords: Neurosciences; Computer science; Bioinformatics; Autism Spectrum Disorder; Dynamical Analysis; Genomics; Machine Learning; Neuronal Models

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APA (6th Edition):

Amatya, D. N. (2019). Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5pd7d65s

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):

Amatya, Debha Narsingh. “Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches.” 2019. Thesis, University of California – San Diego. Accessed August 18, 2019. http://www.escholarship.org/uc/item/5pd7d65s.

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

MLA Handbook (7th Edition):

Amatya, Debha Narsingh. “Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches.” 2019. Web. 18 Aug 2019.

Vancouver:

Amatya DN. Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2019 Aug 18]. Available from: http://www.escholarship.org/uc/item/5pd7d65s.

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

Council of Science Editors:

Amatya DN. Exploring the Spectrum of Autism: Machine Learning and Dynamical Analysis Approaches. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/5pd7d65s

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


ETH Zürich

12. Mendler-Dünner, Celestine. System-Aware Algorithms For Machine Learning.

Degree: 2019, ETH Zürich

 The design of machine learning algorithms is often conducted in a system-agnostic manner. As a consequence, established methods may not be well aligned with the… (more)

Subjects/Keywords: machine learning; distributed algorithms; GPU acceleration; convex optimization; generalized linear models; heterogeneous system; snap machine learning; parallel algorithms

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APA (6th Edition):

Mendler-Dünner, C. (2019). System-Aware Algorithms For Machine Learning. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/341033

Chicago Manual of Style (16th Edition):

Mendler-Dünner, Celestine. “System-Aware Algorithms For Machine Learning.” 2019. Doctoral Dissertation, ETH Zürich. Accessed August 18, 2019. http://hdl.handle.net/20.500.11850/341033.

MLA Handbook (7th Edition):

Mendler-Dünner, Celestine. “System-Aware Algorithms For Machine Learning.” 2019. Web. 18 Aug 2019.

Vancouver:

Mendler-Dünner C. System-Aware Algorithms For Machine Learning. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/20.500.11850/341033.

Council of Science Editors:

Mendler-Dünner C. System-Aware Algorithms For Machine Learning. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/341033


KTH

13. Fredriksson, Gustav. Restricted Boltzmann Machine as Recommendation Model for Venture Capital.

Degree: Mathematical Statistics, 2019, KTH

Denna studie introducerar restricted Boltzmann machines (RBMs) som rekommendationsmodell i kontexten av riskkapital. Ett nätverk av relationer används som proxy för att modellera investerares… (more)

Subjects/Keywords: Machine learning; statistics; applied mathematics; venture capital; recommendation models; Maskininlärning; statistik; tillämpad matematik; riskkapital; rekommendationsmodeller; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA (6th Edition):

Fredriksson, G. (2019). Restricted Boltzmann Machine as Recommendation Model for Venture Capital. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703

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):

Fredriksson, Gustav. “Restricted Boltzmann Machine as Recommendation Model for Venture Capital.” 2019. Thesis, KTH. Accessed August 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703.

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

MLA Handbook (7th Edition):

Fredriksson, Gustav. “Restricted Boltzmann Machine as Recommendation Model for Venture Capital.” 2019. Web. 18 Aug 2019.

Vancouver:

Fredriksson G. Restricted Boltzmann Machine as Recommendation Model for Venture Capital. [Internet] [Thesis]. KTH; 2019. [cited 2019 Aug 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703.

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

Council of Science Editors:

Fredriksson G. Restricted Boltzmann Machine as Recommendation Model for Venture Capital. [Thesis]. KTH; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252703

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

14. Khan, Mohammed Salman. A Topic Modeling approach for Code Clone Detection.

Degree: 2019, University of North Florida

 In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is… (more)

Subjects/Keywords: Thesis; University of North Florida; UNF; Dissertations, Academic  – UNF  – Master of Science in Computer and Information Sciences; Dissertations, Academic  – UNF  – Computing; code clone; clone detection; topic modeling; machine learning; software refactoring; software engineering; Latent Dirichlet Allocation  – Testing; Topic models  – Testing; Generative statistical models  – Testing; Code clone detection  – Statistical models; Code clone detection  – Software; CloneTM  – Testing; Computer and Systems Architecture; Software Engineering

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APA (6th Edition):

Khan, M. S. (2019). A Topic Modeling approach for Code Clone Detection. (Thesis). University of North Florida. Retrieved from https://digitalcommons.unf.edu/etd/874

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):

Khan, Mohammed Salman. “A Topic Modeling approach for Code Clone Detection.” 2019. Thesis, University of North Florida. Accessed August 18, 2019. https://digitalcommons.unf.edu/etd/874.

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

MLA Handbook (7th Edition):

Khan, Mohammed Salman. “A Topic Modeling approach for Code Clone Detection.” 2019. Web. 18 Aug 2019.

Vancouver:

Khan MS. A Topic Modeling approach for Code Clone Detection. [Internet] [Thesis]. University of North Florida; 2019. [cited 2019 Aug 18]. Available from: https://digitalcommons.unf.edu/etd/874.

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

Council of Science Editors:

Khan MS. A Topic Modeling approach for Code Clone Detection. [Thesis]. University of North Florida; 2019. Available from: https://digitalcommons.unf.edu/etd/874

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


University of Arkansas

15. AlAlawi, Ali Zohair A. Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters.

Degree: MSBME, 2019, University of Arkansas

  Analysis of peripheral venous pressure (PVP) waveforms is a novel method of monitoring intravascular volume. Two cohorts were used to study the hemodynamics change… (more)

Subjects/Keywords: Machine Learning Predition Models; Medical Triage; Minimum Alveolar Concentration; Peripheral Venous Pressure; Propofol; Volume Change Assessment; Biomedical Devices and Instrumentation; Cardiovascular System; Systems and Integrative Engineering

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APA (6th Edition):

AlAlawi, A. Z. A. (2019). Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters. (Masters Thesis). University of Arkansas. Retrieved from https://scholarworks.uark.edu/etd/3243

Chicago Manual of Style (16th Edition):

AlAlawi, Ali Zohair A. “Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters.” 2019. Masters Thesis, University of Arkansas. Accessed August 18, 2019. https://scholarworks.uark.edu/etd/3243.

MLA Handbook (7th Edition):

AlAlawi, Ali Zohair A. “Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters.” 2019. Web. 18 Aug 2019.

Vancouver:

AlAlawi AZA. Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters. [Internet] [Masters thesis]. University of Arkansas; 2019. [cited 2019 Aug 18]. Available from: https://scholarworks.uark.edu/etd/3243.

Council of Science Editors:

AlAlawi AZA. Using Peripheral Venous Pressure Waveforms to Predict Key Hemodynamic Parameters. [Masters Thesis]. University of Arkansas; 2019. Available from: https://scholarworks.uark.edu/etd/3243


Brno University of Technology

16. Urbanczyk, Martin. Webový simulátor fotbalových lig a turnajů .

Degree: 2019, Brno University of Technology

 Tato diplomová práce pojednává o tvorbě webového simulátoru fotbalových soutěží. V rámci této práce byla nastudována problematika systémů fotbalových principů a soutěží. Dále pak problematika… (more)

Subjects/Keywords: fotbal; fotbalové soutěže; simulátor; simulace; strojové učení; regresivní modely; predikce; webová aplikace; Python; SciKit-learn; REST architektura; Node.js; football; football competitions; simulator; simulation; machine learning; regression models; prediction; web application; Python; SciKit-learn; REST architecture; Node.js

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APA (6th Edition):

Urbanczyk, M. (2019). Webový simulátor fotbalových lig a turnajů . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180383

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):

Urbanczyk, Martin. “Webový simulátor fotbalových lig a turnajů .” 2019. Thesis, Brno University of Technology. Accessed August 18, 2019. http://hdl.handle.net/11012/180383.

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

MLA Handbook (7th Edition):

Urbanczyk, Martin. “Webový simulátor fotbalových lig a turnajů .” 2019. Web. 18 Aug 2019.

Vancouver:

Urbanczyk M. Webový simulátor fotbalových lig a turnajů . [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/11012/180383.

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

Council of Science Editors:

Urbanczyk M. Webový simulátor fotbalových lig a turnajů . [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180383

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


Iowa State University

17. Soltani, Mohammadreza. Provable algorithms for nonlinear models in machine learning and signal processing.

Degree: 2019, Iowa State University

 In numerous signal processing and machine learning applications, the problem of signal recovery from a limited number of nonlinear observations is of special interest. These… (more)

Subjects/Keywords: Computational Analysis; Machine Learning; Nonlinear Models; Provable Algorithms; Signal Processing; Statistical Analysis; Computer Sciences; Electrical and Electronics; Statistics and Probability

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APA (6th Edition):

Soltani, M. (2019). Provable algorithms for nonlinear models in machine learning and signal processing. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/17102

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):

Soltani, Mohammadreza. “Provable algorithms for nonlinear models in machine learning and signal processing.” 2019. Thesis, Iowa State University. Accessed August 18, 2019. https://lib.dr.iastate.edu/etd/17102.

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

MLA Handbook (7th Edition):

Soltani, Mohammadreza. “Provable algorithms for nonlinear models in machine learning and signal processing.” 2019. Web. 18 Aug 2019.

Vancouver:

Soltani M. Provable algorithms for nonlinear models in machine learning and signal processing. [Internet] [Thesis]. Iowa State University; 2019. [cited 2019 Aug 18]. Available from: https://lib.dr.iastate.edu/etd/17102.

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

Council of Science Editors:

Soltani M. Provable algorithms for nonlinear models in machine learning and signal processing. [Thesis]. Iowa State University; 2019. Available from: https://lib.dr.iastate.edu/etd/17102

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


University of Tennessee – Knoxville

18. Maksov, Artem Borisovich. Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging.

Degree: 2018, University of Tennessee – Knoxville

 Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials… (more)

Subjects/Keywords: Scanning transmission electron microscopy; scanning tunneling microscopy; artificial intelligence; machine learning; deep learning; probabilistic models

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APA (6th Edition):

Maksov, A. B. (2018). Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5259

Chicago Manual of Style (16th Edition):

Maksov, Artem Borisovich. “Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging.” 2018. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed August 18, 2019. https://trace.tennessee.edu/utk_graddiss/5259.

MLA Handbook (7th Edition):

Maksov, Artem Borisovich. “Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging.” 2018. Web. 18 Aug 2019.

Vancouver:

Maksov AB. Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2018. [cited 2019 Aug 18]. Available from: https://trace.tennessee.edu/utk_graddiss/5259.

Council of Science Editors:

Maksov AB. Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2018. Available from: https://trace.tennessee.edu/utk_graddiss/5259


California State University – Channel Islands

19. Zhang, Zhe. EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques .

Degree: 2018, California State University – Channel Islands

 The purpose of this research is to explore electroencephalogram (EEG) signal features and evaluate the performance of different prediction models. This paper presents the methodologies… (more)

Subjects/Keywords: Computer Science thesis; electroencephalogram; machine learning; statistical models

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APA (6th Edition):

Zhang, Z. (2018). EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques . (Thesis). California State University – Channel Islands. Retrieved from http://hdl.handle.net/10211.3/207496

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):

Zhang, Zhe. “EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques .” 2018. Thesis, California State University – Channel Islands. Accessed August 18, 2019. http://hdl.handle.net/10211.3/207496.

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

MLA Handbook (7th Edition):

Zhang, Zhe. “EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques .” 2018. Web. 18 Aug 2019.

Vancouver:

Zhang Z. EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques . [Internet] [Thesis]. California State University – Channel Islands; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/10211.3/207496.

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

Council of Science Editors:

Zhang Z. EFG Signal Processing and Analysis Using Efficient Machine Learning Techniques . [Thesis]. California State University – Channel Islands; 2018. Available from: http://hdl.handle.net/10211.3/207496

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


University of Illinois – Urbana-Champaign

20. Garshasebi, Behnoush. Machine-learning-based vehicle delay prediction at signalized intersections.

Degree: MS, Civil Engineering, 2018, University of Illinois – Urbana-Champaign

 Delay is one of the critical elements of signalized intersections performance measures. Field delay calculations are usually time-consuming and inefficient and dominantly rely on manual… (more)

Subjects/Keywords: vehicle delay; machine learning; prediction models; data-driven models; delay prediction.

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APA (6th Edition):

Garshasebi, B. (2018). Machine-learning-based vehicle delay prediction at signalized intersections. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102430

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):

Garshasebi, Behnoush. “Machine-learning-based vehicle delay prediction at signalized intersections.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed August 18, 2019. http://hdl.handle.net/2142/102430.

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

MLA Handbook (7th Edition):

Garshasebi, Behnoush. “Machine-learning-based vehicle delay prediction at signalized intersections.” 2018. Web. 18 Aug 2019.

Vancouver:

Garshasebi B. Machine-learning-based vehicle delay prediction at signalized intersections. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2142/102430.

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

Council of Science Editors:

Garshasebi B. Machine-learning-based vehicle delay prediction at signalized intersections. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102430

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

21. Huguenin, Manon. Development and validation of 3-D cloud fields using data fusion and machine learning techniques.

Degree: MS, Aerospace Engineering, 2018, Georgia Tech

 The impact of climate change is projected to significantly increase over the next decades. Consequently, gaining a better understanding of climate change and being able… (more)

Subjects/Keywords: Global climate models; Cloud; Data fusion; Machine learning; Satellite data; A-train

…geolocation, or observed radiances, and to train these models using machine learning techniques… …enhanced the performance of the models, for all machine learning techniques considered. The… …ASDL to develop an approach that leverages data fusion and machine learning techniques to… …dataset against existing data. Such validation demonstrates the potential of a machine learning… …methodology to further validate this machine learning-based approach in the context of study… 

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APA (6th Edition):

Huguenin, M. (2018). Development and validation of 3-D cloud fields using data fusion and machine learning techniques. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60824

Chicago Manual of Style (16th Edition):

Huguenin, Manon. “Development and validation of 3-D cloud fields using data fusion and machine learning techniques.” 2018. Masters Thesis, Georgia Tech. Accessed August 18, 2019. http://hdl.handle.net/1853/60824.

MLA Handbook (7th Edition):

Huguenin, Manon. “Development and validation of 3-D cloud fields using data fusion and machine learning techniques.” 2018. Web. 18 Aug 2019.

Vancouver:

Huguenin M. Development and validation of 3-D cloud fields using data fusion and machine learning techniques. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1853/60824.

Council of Science Editors:

Huguenin M. Development and validation of 3-D cloud fields using data fusion and machine learning techniques. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60824

22. Rahier, Thibaud. Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion.

Degree: Docteur es, Mathématiques appliquées, 2018, Grenoble Alpes

 La prédiction et l'inférence sur des données temporelles sont très souvent effectuées en utilisant uniquement les séries temporelles. Nous sommes convaincus que ces tâches pourraient… (more)

Subjects/Keywords: Apprentissage automatique; Réseaux Bayesiens; Métadonnées; Séries temporelles; Modèles graphiques; Apprentissage de structure; Machine Learning; Bayesian networks; Metadata; Time series; Graphical models; Structure learning; 510

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APA (6th Edition):

Rahier, T. (2018). Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2018GREAM083

Chicago Manual of Style (16th Edition):

Rahier, Thibaud. “Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion.” 2018. Doctoral Dissertation, Grenoble Alpes. Accessed August 18, 2019. http://www.theses.fr/2018GREAM083.

MLA Handbook (7th Edition):

Rahier, Thibaud. “Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion.” 2018. Web. 18 Aug 2019.

Vancouver:

Rahier T. Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2018. [cited 2019 Aug 18]. Available from: http://www.theses.fr/2018GREAM083.

Council of Science Editors:

Rahier T. Réseaux Bayésiens pour fusion de données statiques et temporelles : Bayesian networks for static and temporal data fusion. [Doctoral Dissertation]. Grenoble Alpes; 2018. Available from: http://www.theses.fr/2018GREAM083

23. Tubiana, Jérôme. Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines.

Degree: Docteur es, Physique, 2018, Paris Sciences et Lettres

Les Machines de Boltzmann restreintes (RBM) sont des modèles graphiques capables d’apprendre simultanément une distribution de probabilité et une représentation des données. Malgré leur architecture… (more)

Subjects/Keywords: Physique statistique; Apprentissage automatique; Analyse des séquences de protéines; Systèmes désordonnés; Modèles génératifs; Coévolution; Statistical physics; Machine learning; Protein sequence analysis; Disordered systems; Generative models; Coevolution; 530

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APA (6th Edition):

Tubiana, J. (2018). Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines. (Doctoral Dissertation). Paris Sciences et Lettres. Retrieved from http://www.theses.fr/2018PSLEE039

Chicago Manual of Style (16th Edition):

Tubiana, Jérôme. “Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines.” 2018. Doctoral Dissertation, Paris Sciences et Lettres. Accessed August 18, 2019. http://www.theses.fr/2018PSLEE039.

MLA Handbook (7th Edition):

Tubiana, Jérôme. “Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines.” 2018. Web. 18 Aug 2019.

Vancouver:

Tubiana J. Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines. [Internet] [Doctoral dissertation]. Paris Sciences et Lettres; 2018. [cited 2019 Aug 18]. Available from: http://www.theses.fr/2018PSLEE039.

Council of Science Editors:

Tubiana J. Restricted Boltzmann machines : from compositional representations to protein sequence analysis : Machines de Boltzmann restreintes : des représentations compositionnelles à l'analyse des séquences de protéines. [Doctoral Dissertation]. Paris Sciences et Lettres; 2018. Available from: http://www.theses.fr/2018PSLEE039


Georgia Tech

24. Hrolenok, Brian Paul. Constructing and evaluating executable models of collective behavior.

Degree: PhD, Computer Science, 2018, Georgia Tech

 Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming… (more)

Subjects/Keywords: Executable models; Machine learning; Multiagent systems

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APA (6th Edition):

Hrolenok, B. P. (2018). Constructing and evaluating executable models of collective behavior. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60751

Chicago Manual of Style (16th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Doctoral Dissertation, Georgia Tech. Accessed August 18, 2019. http://hdl.handle.net/1853/60751.

MLA Handbook (7th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Web. 18 Aug 2019.

Vancouver:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1853/60751.

Council of Science Editors:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60751


Université de Montréal

25. Dinh, Laurent. Reparametrization in deep learning .

Degree: 2018, Université de Montréal

Subjects/Keywords: Neural networks; Deep neural networks; Machine learning; Deep learning; Unsupervised learning; Probabilistic modelling; Probabilistic models; Generative modelling; Generative models; Generator networks; Variational inference; Generalization; Reparametrization trick; Réseaux de neurones; Réseaux neuronaux; Réseaux de neurones profonds; Réseaux neuronaux profonds; Apprentissage automatique; Apprentissage profond; Apprentissage non-supervisé; Modélisation probabiliste; Modélisation générative; Modèles probabilistes; Modèles génératifs; Réseaux générateurs; Inférence variationnelle; Généralisation; Astuce de la reparamétrisation

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APA (6th Edition):

Dinh, L. (2018). Reparametrization in deep learning . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/21139

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):

Dinh, Laurent. “Reparametrization in deep learning .” 2018. Thesis, Université de Montréal. Accessed August 18, 2019. http://hdl.handle.net/1866/21139.

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

MLA Handbook (7th Edition):

Dinh, Laurent. “Reparametrization in deep learning .” 2018. Web. 18 Aug 2019.

Vancouver:

Dinh L. Reparametrization in deep learning . [Internet] [Thesis]. Université de Montréal; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1866/21139.

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

Council of Science Editors:

Dinh L. Reparametrization in deep learning . [Thesis]. Université de Montréal; 2018. Available from: http://hdl.handle.net/1866/21139

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


Université de Montréal

26. Dumoulin, Vincent. Representation Learning for Visual Data .

Degree: 2018, Université de Montréal

Subjects/Keywords: Réseaux neuronaux; Apprentissage automatique; Apprentissage de représentations profondes; Apprentissage non supervisé; Modèles à énergie; Calcul par système physique; Modèles génératifs; Réseaux adversariels génératifs; Synthèse d'image; Pastiche automatique; Neural network; Machine learning; Deep learning; Unsupervised learning; Energy-based models; Physical computing; Generative modeling; Generative adversarial network; Image synthesis; Style transfer

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APA (6th Edition):

Dumoulin, V. (2018). Representation Learning for Visual Data . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/21140

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):

Dumoulin, Vincent. “Representation Learning for Visual Data .” 2018. Thesis, Université de Montréal. Accessed August 18, 2019. http://hdl.handle.net/1866/21140.

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

MLA Handbook (7th Edition):

Dumoulin, Vincent. “Representation Learning for Visual Data .” 2018. Web. 18 Aug 2019.

Vancouver:

Dumoulin V. Representation Learning for Visual Data . [Internet] [Thesis]. Université de Montréal; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1866/21140.

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

Council of Science Editors:

Dumoulin V. Representation Learning for Visual Data . [Thesis]. Université de Montréal; 2018. Available from: http://hdl.handle.net/1866/21140

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

27. Kannan, Hariprasad. Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning.

Degree: Docteur es, Traitement du signal et des images, 2018, Paris Saclay

Les relaxations en problème d’optimisation linéaire jouent un rôle central en inférence du maximum a posteriori (map) dans les champs aléatoires de Markov discrets. Nous… (more)

Subjects/Keywords: Vision par ordinateur; Apprentissage automatique; Modèles graphiques; Inférence MAP; Apprentissage non-supervisé; Optimisation numérique; Graphical models; Machine learning; Computer vision; Unsupervised learning; Numerical optimization; MAP inference

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APA (6th Edition):

Kannan, H. (2018). Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2018SACLC067

Chicago Manual of Style (16th Edition):

Kannan, Hariprasad. “Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning.” 2018. Doctoral Dissertation, Paris Saclay. Accessed August 18, 2019. http://www.theses.fr/2018SACLC067.

MLA Handbook (7th Edition):

Kannan, Hariprasad. “Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning.” 2018. Web. 18 Aug 2019.

Vancouver:

Kannan H. Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning. [Internet] [Doctoral dissertation]. Paris Saclay; 2018. [cited 2019 Aug 18]. Available from: http://www.theses.fr/2018SACLC067.

Council of Science Editors:

Kannan H. Quelques applications de l’optimisation numérique aux problèmes d’inférence et d’apprentissage : Few applications of numerical optimization in inference and learning. [Doctoral Dissertation]. Paris Saclay; 2018. Available from: http://www.theses.fr/2018SACLC067


Leiden University

28. Stein, van, B. Data driven modeling & optimization of industrial processes.

Degree: 2018, Leiden University

 Industrial manufacturing processes, such as the production of steel or the stamping of car body parts, are complex semi-batch processes with many process steps, machine(more)

Subjects/Keywords: Machine learning; Data mining; Industrial processes; Optimization; Kriging; Quassian processes; Data driven; Surrogate models; Machine learning; Data mining; Industrial processes; Optimization; Kriging; Quassian processes; Data driven; Surrogate models

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APA (6th Edition):

Stein, van, B. (2018). Data driven modeling & optimization of industrial processes. (Doctoral Dissertation). Leiden University. Retrieved from http://hdl.handle.net/1887/65632

Chicago Manual of Style (16th Edition):

Stein, van, B. “Data driven modeling & optimization of industrial processes.” 2018. Doctoral Dissertation, Leiden University. Accessed August 18, 2019. http://hdl.handle.net/1887/65632.

MLA Handbook (7th Edition):

Stein, van, B. “Data driven modeling & optimization of industrial processes.” 2018. Web. 18 Aug 2019.

Vancouver:

Stein, van B. Data driven modeling & optimization of industrial processes. [Internet] [Doctoral dissertation]. Leiden University; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1887/65632.

Council of Science Editors:

Stein, van B. Data driven modeling & optimization of industrial processes. [Doctoral Dissertation]. Leiden University; 2018. Available from: http://hdl.handle.net/1887/65632


University of Texas – Austin

29. Tandon, Rashish. On structured and distributed learning.

Degree: Computer Sciences, 2018, University of Texas – Austin

 With the growth in size and complexity of data, methods exploiting low-dimensional structure, as well as distributed methods, have been playing an ever important role… (more)

Subjects/Keywords: Machine learning; Graphical models; Ising models; Neighborhood selection; Regression; Linear regression; Kernel ridge regression; Local learning; Stragglers; Coding theory; Gradient coding

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

APA (6th Edition):

Tandon, R. (2018). On structured and distributed learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68175

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):

Tandon, Rashish. “On structured and distributed learning.” 2018. Thesis, University of Texas – Austin. Accessed August 18, 2019. http://hdl.handle.net/2152/68175.

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

MLA Handbook (7th Edition):

Tandon, Rashish. “On structured and distributed learning.” 2018. Web. 18 Aug 2019.

Vancouver:

Tandon R. On structured and distributed learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2152/68175.

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

Council of Science Editors:

Tandon R. On structured and distributed learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68175

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


University of Texas – Austin

30. Zheng, Hanyue. KKBox subscription prediction : an application of machine learning methods.

Degree: Statistics, 2018, University of Texas – Austin

 This report used datasets from a Kaggle competition which aims to develop machine learning models to predict if users of a music app called KKBox… (more)

Subjects/Keywords: Machine learning; Classification; Machine learning models; Machine learning model development; Machine learning classification models; Machine learning model performance

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zheng, H. (2018). KKBox subscription prediction : an application of machine learning methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67638

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):

Zheng, Hanyue. “KKBox subscription prediction : an application of machine learning methods.” 2018. Thesis, University of Texas – Austin. Accessed August 18, 2019. http://hdl.handle.net/2152/67638.

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

MLA Handbook (7th Edition):

Zheng, Hanyue. “KKBox subscription prediction : an application of machine learning methods.” 2018. Web. 18 Aug 2019.

Vancouver:

Zheng H. KKBox subscription prediction : an application of machine learning methods. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/2152/67638.

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

Council of Science Editors:

Zheng H. KKBox subscription prediction : an application of machine learning methods. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67638

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

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