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You searched for subject:(Semi supervised learning). Showing records 1 – 30 of 254 total matches.

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1. Zhao, Xuran. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.

Degree: Docteur es, Signal et images, 2013, Paris, ENST

Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments… (more)

Subjects/Keywords: Apprentissage semi-supervisé; Semi-supervised learning

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APA (6th Edition):

Zhao, X. (2013). Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0061

Chicago Manual of Style (16th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Doctoral Dissertation, Paris, ENST. Accessed April 12, 2021. http://www.theses.fr/2013ENST0061.

MLA Handbook (7th Edition):

Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Web. 12 Apr 2021.

Vancouver:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2021 Apr 12]. Available from: http://www.theses.fr/2013ENST0061.

Council of Science Editors:

Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0061


University of Texas – Austin

2. Joshi, Shalmali Dilip. Constraint based approaches to interpretable and semi-supervised machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social… (more)

Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning

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APA (6th Edition):

Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259

Chicago Manual of Style (16th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed April 12, 2021. http://dx.doi.org/10.26153/tsw/1259.

MLA Handbook (7th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 12 Apr 2021.

Vancouver:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Apr 12]. Available from: http://dx.doi.org/10.26153/tsw/1259.

Council of Science Editors:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259


Baylor University

3. -1697-5430. Semi-supervised learning for electrocardiography signal classification.

Degree: M.S.E.C.E., Baylor University. Dept. of Electrical & Computer Engineering., 2018, Baylor University

 An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected… (more)

Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition

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APA (6th Edition):

-1697-5430. (2018). Semi-supervised learning for electrocardiography signal classification. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Masters Thesis, Baylor University. Accessed April 12, 2021. http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Web. 12 Apr 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Internet] [Masters thesis]. Baylor University; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Masters Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Wayne State University

4. Hailat, Zeyad. Deep Learning Methods For Visual Object Recognition.

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

  Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large… (more)

Subjects/Keywords: artificial intelligence; deep learning; machine learning; self-learning; semi-supervised learning; supervised learning; Computer Sciences

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APA (6th Edition):

Hailat, Z. (2018). Deep Learning Methods For Visual Object Recognition. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/2026

Chicago Manual of Style (16th Edition):

Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Doctoral Dissertation, Wayne State University. Accessed April 12, 2021. https://digitalcommons.wayne.edu/oa_dissertations/2026.

MLA Handbook (7th Edition):

Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Web. 12 Apr 2021.

Vancouver:

Hailat Z. Deep Learning Methods For Visual Object Recognition. [Internet] [Doctoral dissertation]. Wayne State University; 2018. [cited 2021 Apr 12]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026.

Council of Science Editors:

Hailat Z. Deep Learning Methods For Visual Object Recognition. [Doctoral Dissertation]. Wayne State University; 2018. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026


University of Illinois – Urbana-Champaign

5. He, Shibi. Reinforced co-learning for semi-supervised ranking.

Degree: MS, Computer Science, 2018, University of Illinois – Urbana-Champaign

Learning to rank is vital to information retrieval and recommendation systems. Directly optimizing the listwise evaluation measure such as normalized discounted cumulative gain (NDCG) is… (more)

Subjects/Keywords: Learning to rank; Semi-supervised Learning; Reinforcement Learning; Machine Learning

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APA (6th Edition):

He, S. (2018). Reinforced co-learning for semi-supervised ranking. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102868

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):

He, Shibi. “Reinforced co-learning for semi-supervised ranking.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed April 12, 2021. http://hdl.handle.net/2142/102868.

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

MLA Handbook (7th Edition):

He, Shibi. “Reinforced co-learning for semi-supervised ranking.” 2018. Web. 12 Apr 2021.

Vancouver:

He S. Reinforced co-learning for semi-supervised ranking. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2142/102868.

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

Council of Science Editors:

He S. Reinforced co-learning for semi-supervised ranking. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102868

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


Delft University of Technology

6. Jurasiński, Karol (author). Towards deeper understanding of semi-supervised learning with variational autoencoders.

Degree: 2019, Delft University of Technology

 Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled… (more)

Subjects/Keywords: semi-supervised learning; variational inference; deep learning; machine learning

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APA (6th Edition):

Jurasiński, K. (. (2019). Towards deeper understanding of semi-supervised learning with variational autoencoders. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb

Chicago Manual of Style (16th Edition):

Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Masters Thesis, Delft University of Technology. Accessed April 12, 2021. http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.

MLA Handbook (7th Edition):

Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Web. 12 Apr 2021.

Vancouver:

Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 12]. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.

Council of Science Editors:

Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb

7. Håkansson, Henrik. Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning .

Degree: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper, 2020, Chalmers University of Technology

 An autoencoder is a neural network for unsupervised learning, which consists of two parts: an encoder and a decoder. The encoder uses data as input,… (more)

Subjects/Keywords: semi-supervised learning; distributed machine learning; deep learning; autoencoder; ADMM

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APA (6th Edition):

Håkansson, H. (2020). Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301427

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):

Håkansson, Henrik. “Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning .” 2020. Thesis, Chalmers University of Technology. Accessed April 12, 2021. http://hdl.handle.net/20.500.12380/301427.

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

MLA Handbook (7th Edition):

Håkansson, Henrik. “Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning .” 2020. Web. 12 Apr 2021.

Vancouver:

Håkansson H. Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/20.500.12380/301427.

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

Council of Science Editors:

Håkansson H. Training Multi-Tasking Neural Network using ADMM: Analysing Autoencoder-Based Semi-Supervised Learning . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301427

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


Penn State University

8. Biyani, Prakhar. Analyzing Subjectivity and Sentiment of Online Forums.

Degree: 2014, Penn State University

 Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs… (more)

Subjects/Keywords: Subjectivity analysis; sentiment analysis; classification; supervised learning; semi-supervised learning; online forums

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APA (6th Edition):

Biyani, P. (2014). Analyzing Subjectivity and Sentiment of Online Forums. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22850

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):

Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Thesis, Penn State University. Accessed April 12, 2021. https://submit-etda.libraries.psu.edu/catalog/22850.

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

MLA Handbook (7th Edition):

Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Web. 12 Apr 2021.

Vancouver:

Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Apr 12]. Available from: https://submit-etda.libraries.psu.edu/catalog/22850.

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

Council of Science Editors:

Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22850

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

9. Byun, Byungki. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.

Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech

 This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having… (more)

Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7

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APA (6th Edition):

Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597

Chicago Manual of Style (16th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed April 12, 2021. http://hdl.handle.net/1853/43597.

MLA Handbook (7th Edition):

Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 12 Apr 2021.

Vancouver:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1853/43597.

Council of Science Editors:

Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597


University of Houston

10. Wu, Hao. Semi-supervised and Deep Learning for Hyperspectral Image Analysis.

Degree: PhD, Electrical Engineering, 2017, University of Houston

 Hyperspectral imaging is a technique which uses hyperspectral sensors to collect spectral information across the electromagnetic spectrum for each pixel in the image of a… (more)

Subjects/Keywords: Semi-supervised learning; Deep learning; Dirichlet process mixture; Hyperspectral imaging

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APA (6th Edition):

Wu, H. (2017). Semi-supervised and Deep Learning for Hyperspectral Image Analysis. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4620

Chicago Manual of Style (16th Edition):

Wu, Hao. “Semi-supervised and Deep Learning for Hyperspectral Image Analysis.” 2017. Doctoral Dissertation, University of Houston. Accessed April 12, 2021. http://hdl.handle.net/10657/4620.

MLA Handbook (7th Edition):

Wu, Hao. “Semi-supervised and Deep Learning for Hyperspectral Image Analysis.” 2017. Web. 12 Apr 2021.

Vancouver:

Wu H. Semi-supervised and Deep Learning for Hyperspectral Image Analysis. [Internet] [Doctoral dissertation]. University of Houston; 2017. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10657/4620.

Council of Science Editors:

Wu H. Semi-supervised and Deep Learning for Hyperspectral Image Analysis. [Doctoral Dissertation]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4620


Rice University

11. Nguyen, Minh Tan. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.

Degree: MS, Engineering, 2018, Rice University

 A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks such as visual object… (more)

Subjects/Keywords: deep learning; deep convolutional network; generative model; semi-supervised learning

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APA (6th Edition):

Nguyen, M. T. (2018). The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105801

Chicago Manual of Style (16th Edition):

Nguyen, Minh Tan. “The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.” 2018. Masters Thesis, Rice University. Accessed April 12, 2021. http://hdl.handle.net/1911/105801.

MLA Handbook (7th Edition):

Nguyen, Minh Tan. “The Deep Rendering Model: Bridging Theory and Practice in Deep Learning.” 2018. Web. 12 Apr 2021.

Vancouver:

Nguyen MT. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. [Internet] [Masters thesis]. Rice University; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1911/105801.

Council of Science Editors:

Nguyen MT. The Deep Rendering Model: Bridging Theory and Practice in Deep Learning. [Masters Thesis]. Rice University; 2018. Available from: http://hdl.handle.net/1911/105801


Georgia Tech

12. Alfarraj, Motaz A. Learning from seismic data to characterize subsurface volumes.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 The exponential growth of collected data from seismic surveys makes it impossible for interpreters to manually inspect, analyze and annotate all collected data. Deep learning(more)

Subjects/Keywords: Deep learning; Semi-supervised learning; Sequence modeling; Subsurface characterization; Seismic inversion

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APA (6th Edition):

Alfarraj, M. A. (2019). Learning from seismic data to characterize subsurface volumes. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62310

Chicago Manual of Style (16th Edition):

Alfarraj, Motaz A. “Learning from seismic data to characterize subsurface volumes.” 2019. Doctoral Dissertation, Georgia Tech. Accessed April 12, 2021. http://hdl.handle.net/1853/62310.

MLA Handbook (7th Edition):

Alfarraj, Motaz A. “Learning from seismic data to characterize subsurface volumes.” 2019. Web. 12 Apr 2021.

Vancouver:

Alfarraj MA. Learning from seismic data to characterize subsurface volumes. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1853/62310.

Council of Science Editors:

Alfarraj MA. Learning from seismic data to characterize subsurface volumes. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62310


University of Illinois – Urbana-Champaign

13. Ji, Ming. Semi-supervised learning and relevance search on networked data.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 Real-world data entities are often connected by meaningful relationships, forming large-scale networks. With the rapid growth of social networks and online relational data, it is… (more)

Subjects/Keywords: Data Mining; Machine Learning; Semi-supervised Learning; Search; Heterogeneous Networks; Graphs

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APA (6th Edition):

Ji, M. (2014). Semi-supervised learning and relevance search on networked data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46856

Chicago Manual of Style (16th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 12, 2021. http://hdl.handle.net/2142/46856.

MLA Handbook (7th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Web. 12 Apr 2021.

Vancouver:

Ji M. Semi-supervised learning and relevance search on networked data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2142/46856.

Council of Science Editors:

Ji M. Semi-supervised learning and relevance search on networked data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46856


University of Melbourne

14. Ganji, Mohadeseh. Semi-supervised community detection and clustering.

Degree: 2017, University of Melbourne

 Data clustering and community detection in networks are two important tasks in machine learning which aim to group the data into similar objects or densely… (more)

Subjects/Keywords: machine learning; optimization; semi-supervised learning; clustering; community detection

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APA (6th Edition):

Ganji, M. (2017). Semi-supervised community detection and clustering. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/208821

Chicago Manual of Style (16th Edition):

Ganji, Mohadeseh. “Semi-supervised community detection and clustering.” 2017. Doctoral Dissertation, University of Melbourne. Accessed April 12, 2021. http://hdl.handle.net/11343/208821.

MLA Handbook (7th Edition):

Ganji, Mohadeseh. “Semi-supervised community detection and clustering.” 2017. Web. 12 Apr 2021.

Vancouver:

Ganji M. Semi-supervised community detection and clustering. [Internet] [Doctoral dissertation]. University of Melbourne; 2017. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/11343/208821.

Council of Science Editors:

Ganji M. Semi-supervised community detection and clustering. [Doctoral Dissertation]. University of Melbourne; 2017. Available from: http://hdl.handle.net/11343/208821


Iowa State University

15. Hamdan, Muhammad. Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM).

Degree: 2020, Iowa State University

Supervised learning is the workhorse for regression and classification tasks, but the standard approach presumes ground truth (label) for every measurement. In real world applications,… (more)

Subjects/Keywords: Computer vision; Deep learning; Semi-supervised learning; Sparse data; Video processing

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APA (6th Edition):

Hamdan, M. (2020). Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM). (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/18321

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):

Hamdan, Muhammad. “Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM).” 2020. Thesis, Iowa State University. Accessed April 12, 2021. https://lib.dr.iastate.edu/etd/18321.

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

MLA Handbook (7th Edition):

Hamdan, Muhammad. “Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM).” 2020. Web. 12 Apr 2021.

Vancouver:

Hamdan M. Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM). [Internet] [Thesis]. Iowa State University; 2020. [cited 2021 Apr 12]. Available from: https://lib.dr.iastate.edu/etd/18321.

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

Council of Science Editors:

Hamdan M. Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM). [Thesis]. Iowa State University; 2020. Available from: https://lib.dr.iastate.edu/etd/18321

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


Georgia Tech

16. Berlind, Christopher. New insights on the power of active learning.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in… (more)

Subjects/Keywords: Machine learning; Learning theory; Active learning; Semi-supervised learning; Domain adaptation; Large margin learning

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APA (6th Edition):

Berlind, C. (2015). New insights on the power of active learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53948

Chicago Manual of Style (16th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 12, 2021. http://hdl.handle.net/1853/53948.

MLA Handbook (7th Edition):

Berlind, Christopher. “New insights on the power of active learning.” 2015. Web. 12 Apr 2021.

Vancouver:

Berlind C. New insights on the power of active learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1853/53948.

Council of Science Editors:

Berlind C. New insights on the power of active learning. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53948


University of Louisville

17. Emara, Wael. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.

Degree: PhD, 2012, University of Louisville

 The revolution in information technology and the explosion in the use of computing devices in people's everyday activities has forever changed the perspective of the… (more)

Subjects/Keywords: Machine learning; online learning; semi-supervised learning; Never-Ending Learning; submodular optimization; Support Vector Machines

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APA (6th Edition):

Emara, W. (2012). A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404

Chicago Manual of Style (16th Edition):

Emara, Wael. “A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.” 2012. Doctoral Dissertation, University of Louisville. Accessed April 12, 2021. 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404.

MLA Handbook (7th Edition):

Emara, Wael. “A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning.” 2012. Web. 12 Apr 2021.

Vancouver:

Emara W. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. [Internet] [Doctoral dissertation]. University of Louisville; 2012. [cited 2021 Apr 12]. Available from: 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404.

Council of Science Editors:

Emara W. A submodular optimization framework for never-ending learning : semi-supervised, online, and active learning. [Doctoral Dissertation]. University of Louisville; 2012. Available from: 10.18297/etd/404 ; https://ir.library.louisville.edu/etd/404


King Abdullah University of Science and Technology

18. Akujuobi, Uchenna Thankgod. Learning from Scholarly Attributed Graphs for Scientific Discovery.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2020, King Abdullah University of Science and Technology

 Research and experimentation in various scientific fields are based on the knowledge and ideas from scholarly literature. The advancement of research and development has, thus,… (more)

Subjects/Keywords: semi-supervised learning; graph-based learning; hypothesis generation; reinforcement learning; machine learning; artificial intelligence

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APA (6th Edition):

Akujuobi, U. T. (2020). Learning from Scholarly Attributed Graphs for Scientific Discovery. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/665605

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):

Akujuobi, Uchenna Thankgod. “Learning from Scholarly Attributed Graphs for Scientific Discovery.” 2020. Thesis, King Abdullah University of Science and Technology. Accessed April 12, 2021. http://hdl.handle.net/10754/665605.

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

MLA Handbook (7th Edition):

Akujuobi, Uchenna Thankgod. “Learning from Scholarly Attributed Graphs for Scientific Discovery.” 2020. Web. 12 Apr 2021.

Vancouver:

Akujuobi UT. Learning from Scholarly Attributed Graphs for Scientific Discovery. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2020. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10754/665605.

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

Council of Science Editors:

Akujuobi UT. Learning from Scholarly Attributed Graphs for Scientific Discovery. [Thesis]. King Abdullah University of Science and Technology; 2020. Available from: http://hdl.handle.net/10754/665605

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


University of Melbourne

19. Wang, Wei. Exploiting Gaussian assumption for improving generative models in machine learning.

Degree: 2018, University of Melbourne

 Generative models are one of the most promising approaches in machine learning. This thesis explores the generative models in supervised, semi-supervised and unsupervised learning applications… (more)

Subjects/Keywords: generative models; feature extraction; supervised learning; semi-supervised learning; unsupervised learning; gait analysis; image generation; representation learning; model interpretation

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APA (6th Edition):

Wang, W. (2018). Exploiting Gaussian assumption for improving generative models in machine learning. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/220276

Chicago Manual of Style (16th Edition):

Wang, Wei. “Exploiting Gaussian assumption for improving generative models in machine learning.” 2018. Doctoral Dissertation, University of Melbourne. Accessed April 12, 2021. http://hdl.handle.net/11343/220276.

MLA Handbook (7th Edition):

Wang, Wei. “Exploiting Gaussian assumption for improving generative models in machine learning.” 2018. Web. 12 Apr 2021.

Vancouver:

Wang W. Exploiting Gaussian assumption for improving generative models in machine learning. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/11343/220276.

Council of Science Editors:

Wang W. Exploiting Gaussian assumption for improving generative models in machine learning. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/220276


University of Florida

20. Dobbins, Peter J. Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS).

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2017, University of Florida

 This work performs scene analysis, representing and understanding the elements contained in a defined area under the ground. Elements of interest include: the ground layer,… (more)

Subjects/Keywords: constrained-clustering  – ground-penetrating-radar  – image-segmentation  – markov-random-field  – scene-analysis  – semi-supervised-clustering  – semi-supervised-learning

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APA (6th Edition):

Dobbins, P. J. (2017). Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS). (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0051788

Chicago Manual of Style (16th Edition):

Dobbins, Peter J. “Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS).” 2017. Doctoral Dissertation, University of Florida. Accessed April 12, 2021. https://ufdc.ufl.edu/UFE0051788.

MLA Handbook (7th Edition):

Dobbins, Peter J. “Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS).” 2017. Web. 12 Apr 2021.

Vancouver:

Dobbins PJ. Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS). [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2021 Apr 12]. Available from: https://ufdc.ufl.edu/UFE0051788.

Council of Science Editors:

Dobbins PJ. Scene Analysis Using the Markov Ground Region Segmentation System (MGRSS). [Doctoral Dissertation]. University of Florida; 2017. Available from: https://ufdc.ufl.edu/UFE0051788

21. Tokmakov, Pavel. Apprentissage à partir du mouvement : Learning from motion.

Degree: Docteur es, Informatique, 2018, Université Grenoble Alpes (ComUE)

 L’apprentissage faiblement supervisé cherche à réduire au minimum l’effort humain requis pour entrainer les modèles de l’état de l’art. Cette technique permet de tirer parti… (more)

Subjects/Keywords: Apprentissage; Semi-Supervisé; Reconnaissance des objets; Semi-Supervised; Learning; Recognizing Objects; 004

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APA (6th Edition):

Tokmakov, P. (2018). Apprentissage à partir du mouvement : Learning from motion. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2018GREAM031

Chicago Manual of Style (16th Edition):

Tokmakov, Pavel. “Apprentissage à partir du mouvement : Learning from motion.” 2018. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed April 12, 2021. http://www.theses.fr/2018GREAM031.

MLA Handbook (7th Edition):

Tokmakov, Pavel. “Apprentissage à partir du mouvement : Learning from motion.” 2018. Web. 12 Apr 2021.

Vancouver:

Tokmakov P. Apprentissage à partir du mouvement : Learning from motion. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2018. [cited 2021 Apr 12]. Available from: http://www.theses.fr/2018GREAM031.

Council of Science Editors:

Tokmakov P. Apprentissage à partir du mouvement : Learning from motion. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2018. Available from: http://www.theses.fr/2018GREAM031


McMaster University

22. Li, Huaying. Semi-supervised Information Fusion for Clustering, Classification and Detection Applications.

Degree: PhD, 2017, McMaster University

 Information fusion techniques have been widely applied in many applications including clustering, classification, detection and etc. The major objective is to improve the performance using… (more)

Subjects/Keywords: Information fusion; Semi-supervised learning; Detection; Clustering; New Cluster Detection

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APA (6th Edition):

Li, H. (2017). Semi-supervised Information Fusion for Clustering, Classification and Detection Applications. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/21975

Chicago Manual of Style (16th Edition):

Li, Huaying. “Semi-supervised Information Fusion for Clustering, Classification and Detection Applications.” 2017. Doctoral Dissertation, McMaster University. Accessed April 12, 2021. http://hdl.handle.net/11375/21975.

MLA Handbook (7th Edition):

Li, Huaying. “Semi-supervised Information Fusion for Clustering, Classification and Detection Applications.” 2017. Web. 12 Apr 2021.

Vancouver:

Li H. Semi-supervised Information Fusion for Clustering, Classification and Detection Applications. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/11375/21975.

Council of Science Editors:

Li H. Semi-supervised Information Fusion for Clustering, Classification and Detection Applications. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/21975


University of Washington

23. Kosel, Alison. Local Estimation of Patient Prognosis.

Degree: PhD, 2016, University of Washington

 Statistical methods that can provide patients and their healthcare providers with individual predictions are needed so that informed medical decisions can be made. Ideally an… (more)

Subjects/Keywords: local prediction; non-parametric; semi-supervised learning; Biostatistics; biostatistics

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APA (6th Edition):

Kosel, A. (2016). Local Estimation of Patient Prognosis. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/35541

Chicago Manual of Style (16th Edition):

Kosel, Alison. “Local Estimation of Patient Prognosis.” 2016. Doctoral Dissertation, University of Washington. Accessed April 12, 2021. http://hdl.handle.net/1773/35541.

MLA Handbook (7th Edition):

Kosel, Alison. “Local Estimation of Patient Prognosis.” 2016. Web. 12 Apr 2021.

Vancouver:

Kosel A. Local Estimation of Patient Prognosis. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1773/35541.

Council of Science Editors:

Kosel A. Local Estimation of Patient Prognosis. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/35541


Delft University of Technology

24. Smalbil, Jos (author). Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.

Degree: 2020, Delft University of Technology

In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic… (more)

Subjects/Keywords: text mining; label noise; text classification; semi-supervised learning

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APA (6th Edition):

Smalbil, J. (. (2020). Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0

Chicago Manual of Style (16th Edition):

Smalbil, Jos (author). “Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.” 2020. Masters Thesis, Delft University of Technology. Accessed April 12, 2021. http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0.

MLA Handbook (7th Edition):

Smalbil, Jos (author). “Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.” 2020. Web. 12 Apr 2021.

Vancouver:

Smalbil J(. Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Apr 12]. Available from: http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0.

Council of Science Editors:

Smalbil J(. Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0


Princeton University

25. Hoffman, John. Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys .

Degree: PhD, 2019, Princeton University

 Wide-field variability surveys like HAT (Bakos et al., 2004, 2013), OGLE (Udalski et al., 1994), Pan-STARRs (Chambers et al., 2016a), ASAS (Pojmanski, 1997; Jayas- inghe… (more)

Subjects/Keywords: Machine learning; RR Lyrae; Semi-supervised; Time-series; Variable stars

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APA (6th Edition):

Hoffman, J. (2019). Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp014f16c5604

Chicago Manual of Style (16th Edition):

Hoffman, John. “Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys .” 2019. Doctoral Dissertation, Princeton University. Accessed April 12, 2021. http://arks.princeton.edu/ark:/88435/dsp014f16c5604.

MLA Handbook (7th Edition):

Hoffman, John. “Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys .” 2019. Web. 12 Apr 2021.

Vancouver:

Hoffman J. Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys . [Internet] [Doctoral dissertation]. Princeton University; 2019. [cited 2021 Apr 12]. Available from: http://arks.princeton.edu/ark:/88435/dsp014f16c5604.

Council of Science Editors:

Hoffman J. Bayesian Techniques for Finding and Characterizing Variable Stars: Application to the Hungarian-made Automated Telescope Surveys . [Doctoral Dissertation]. Princeton University; 2019. Available from: http://arks.princeton.edu/ark:/88435/dsp014f16c5604


University of Notre Dame

26. Jeremiah Ross Barr. Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>.

Degree: Computer Science and Engineering, 2014, University of Notre Dame

  The 2013 Boston Marathon investigation presented an ample opportunity to apply advanced face recognition technology. Unfortunately, the crowded scenes proved too difficult for existing… (more)

Subjects/Keywords: clustering; semi-supervised learning; face detection; cluster analysis; Face recognition

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APA (6th Edition):

Barr, J. R. (2014). Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/m039k358m2d

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):

Barr, Jeremiah Ross. “Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>.” 2014. Thesis, University of Notre Dame. Accessed April 12, 2021. https://curate.nd.edu/show/m039k358m2d.

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

MLA Handbook (7th Edition):

Barr, Jeremiah Ross. “Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>.” 2014. Web. 12 Apr 2021.

Vancouver:

Barr JR. Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>. [Internet] [Thesis]. University of Notre Dame; 2014. [cited 2021 Apr 12]. Available from: https://curate.nd.edu/show/m039k358m2d.

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

Council of Science Editors:

Barr JR. Gallery-Free Methods for Detecting and Recognizing People and Groups of Interest in the Wild</h1>. [Thesis]. University of Notre Dame; 2014. Available from: https://curate.nd.edu/show/m039k358m2d

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


University of Illinois – Urbana-Champaign

27. Huang, Jui Ting. Semi-supervised learning for acoustic and prosodic modeling in speech applications.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 Enormous amounts of audio recordings of human speech are essential ingredients for building reliable statistical models for many speech applications, such as automatic speech recognition… (more)

Subjects/Keywords: Semi-Supervised Learning; Speech Recognition; Acoustic Modeling; Prosodic Modeling

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APA (6th Edition):

Huang, J. T. (2012). Semi-supervised learning for acoustic and prosodic modeling in speech applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32006

Chicago Manual of Style (16th Edition):

Huang, Jui Ting. “Semi-supervised learning for acoustic and prosodic modeling in speech applications.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 12, 2021. http://hdl.handle.net/2142/32006.

MLA Handbook (7th Edition):

Huang, Jui Ting. “Semi-supervised learning for acoustic and prosodic modeling in speech applications.” 2012. Web. 12 Apr 2021.

Vancouver:

Huang JT. Semi-supervised learning for acoustic and prosodic modeling in speech applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/2142/32006.

Council of Science Editors:

Huang JT. Semi-supervised learning for acoustic and prosodic modeling in speech applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32006


University of Minnesota

28. Bermperidis, Dimitrios. Active and Adaptive Techniques for Learning over Large-Scale Graphs.

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

 Behind every complex system be it physical, social, biological, or man-made, there is an intricate network encoding the interactions between its components. Learning over large-scale… (more)

Subjects/Keywords: Active Learning; Diffusions; Embedding; Scalable; Semi-supervised; Unsupervised

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APA (6th Edition):

Bermperidis, D. (2019). Active and Adaptive Techniques for Learning over Large-Scale Graphs. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206331

Chicago Manual of Style (16th Edition):

Bermperidis, Dimitrios. “Active and Adaptive Techniques for Learning over Large-Scale Graphs.” 2019. Doctoral Dissertation, University of Minnesota. Accessed April 12, 2021. http://hdl.handle.net/11299/206331.

MLA Handbook (7th Edition):

Bermperidis, Dimitrios. “Active and Adaptive Techniques for Learning over Large-Scale Graphs.” 2019. Web. 12 Apr 2021.

Vancouver:

Bermperidis D. Active and Adaptive Techniques for Learning over Large-Scale Graphs. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/11299/206331.

Council of Science Editors:

Bermperidis D. Active and Adaptive Techniques for Learning over Large-Scale Graphs. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206331


University of Waterloo

29. Smart, Michael. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.

Degree: 2016, University of Waterloo

 Lane detection is a fundamental and challenging task in autonomous driving and must be performed safely and robustly to avoid catastrophic failures. Current methods do… (more)

Subjects/Keywords: Autonomous Driving; Dynamic Bayesian Networks; Lane Detection; Semi-supervised Machine Learning

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APA (6th Edition):

Smart, M. (2016). Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10454

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):

Smart, Michael. “Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.” 2016. Thesis, University of Waterloo. Accessed April 12, 2021. http://hdl.handle.net/10012/10454.

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

MLA Handbook (7th Edition):

Smart, Michael. “Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving.” 2016. Web. 12 Apr 2021.

Vancouver:

Smart M. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/10012/10454.

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

Council of Science Editors:

Smart M. Robust Bayesian Detection and Tracking of Lane Boundary Markings for Autonomous Driving. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10454

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


University of Maryland

30. Klimkowski, Benjamin Harold. ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION.

Degree: Computer Science, 2014, University of Maryland

 This thesis addresses the use of a semi-supervised learning (SSL) method in an intrusion detection setting. Specifically, this thesis illustrates the potential benefits and difficulties… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Journalism; Intrusion Detection; Machine learning; Semi-supervised

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APA (6th Edition):

Klimkowski, B. H. (2014). ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/15393

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):

Klimkowski, Benjamin Harold. “ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION.” 2014. Thesis, University of Maryland. Accessed April 12, 2021. http://hdl.handle.net/1903/15393.

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

MLA Handbook (7th Edition):

Klimkowski, Benjamin Harold. “ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION.” 2014. Web. 12 Apr 2021.

Vancouver:

Klimkowski BH. ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION. [Internet] [Thesis]. University of Maryland; 2014. [cited 2021 Apr 12]. Available from: http://hdl.handle.net/1903/15393.

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

Council of Science Editors:

Klimkowski BH. ANALYSIS OF A SEMI-SUPERVISED LEARNING APPROACH TO INTRUSION DETECTION. [Thesis]. University of Maryland; 2014. Available from: http://hdl.handle.net/1903/15393

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] [8] [9]

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