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You searched for subject:(Domain Adaptation). Showing records 1 – 30 of 117 total matches.

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1. Liu Xinran. Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

Subjects/Keywords: Domain Adaptation

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

APA (6th Edition):

Xinran, L. (n.d.). Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/12478

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Xinran, Liu. “Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed November 27, 2020. http://hdl.handle.net/10061/12478.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Xinran, Liu. “Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ.” Web. 27 Nov 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Xinran L. Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10061/12478.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Xinran L. Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation : 文分類のためのドメイン適応:構造化アブストラクト生成; ブン ブンルイ ノ タメ ノ ドメイン テキオウ : コウゾウ カ アブストラクト セイセイ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/12478

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.


University of Houston

2. Pampana, Renuka 1990-. A New Approach To Domain Adaptation Applied To Supernova Photometric Classification.

Degree: MS, Computer Science, 2016, University of Houston

 Supernova Type Ia plays a vital role in the measurement of the cosmological parameters. It is used as ‘standard candles’ for measuring extragalactic distances. There… (more)

Subjects/Keywords: Supernova; Domain adaptation; Active learning

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

APA (6th Edition):

Pampana, R. 1. (2016). A New Approach To Domain Adaptation Applied To Supernova Photometric Classification. (Masters Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/1495

Chicago Manual of Style (16th Edition):

Pampana, Renuka 1990-. “A New Approach To Domain Adaptation Applied To Supernova Photometric Classification.” 2016. Masters Thesis, University of Houston. Accessed November 27, 2020. http://hdl.handle.net/10657/1495.

MLA Handbook (7th Edition):

Pampana, Renuka 1990-. “A New Approach To Domain Adaptation Applied To Supernova Photometric Classification.” 2016. Web. 27 Nov 2020.

Vancouver:

Pampana R1. A New Approach To Domain Adaptation Applied To Supernova Photometric Classification. [Internet] [Masters thesis]. University of Houston; 2016. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10657/1495.

Council of Science Editors:

Pampana R1. A New Approach To Domain Adaptation Applied To Supernova Photometric Classification. [Masters Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/1495


University of Houston

3. Mehrparvar, Behrang 1984-. Conceptual Domain Adaptation using Deep Learning.

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

Domain adaptation scenarios have been successively gaining attention in practical applications of machine learning. Here, the source distribution in which the classifier is trained, differs… (more)

Subjects/Keywords: Domain adaptation; Deep learning

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

APA (6th Edition):

Mehrparvar, B. 1. (2017). Conceptual Domain Adaptation using Deep Learning. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4808

Chicago Manual of Style (16th Edition):

Mehrparvar, Behrang 1984-. “Conceptual Domain Adaptation using Deep Learning.” 2017. Doctoral Dissertation, University of Houston. Accessed November 27, 2020. http://hdl.handle.net/10657/4808.

MLA Handbook (7th Edition):

Mehrparvar, Behrang 1984-. “Conceptual Domain Adaptation using Deep Learning.” 2017. Web. 27 Nov 2020.

Vancouver:

Mehrparvar B1. Conceptual Domain Adaptation using Deep Learning. [Internet] [Doctoral dissertation]. University of Houston; 2017. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10657/4808.

Council of Science Editors:

Mehrparvar B1. Conceptual Domain Adaptation using Deep Learning. [Doctoral Dissertation]. University of Houston; 2017. Available from: http://hdl.handle.net/10657/4808


Rutgers University

4. Babagholami Mohamadabadi, Behnam. Unsupervised visual domain adaptation: a probabilistic approach.

Degree: PhD, Computer Science, 2020, Rutgers University

 Artificial intelligent and machine learning technologies have already achieved significant success in various applications (computer vision, natural language processing, speech recognition, etc.). Such methods work… (more)

Subjects/Keywords: Domain adaptation; Artificial intelligence

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APA (6th Edition):

Babagholami Mohamadabadi, B. (2020). Unsupervised visual domain adaptation: a probabilistic approach. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62650/

Chicago Manual of Style (16th Edition):

Babagholami Mohamadabadi, Behnam. “Unsupervised visual domain adaptation: a probabilistic approach.” 2020. Doctoral Dissertation, Rutgers University. Accessed November 27, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62650/.

MLA Handbook (7th Edition):

Babagholami Mohamadabadi, Behnam. “Unsupervised visual domain adaptation: a probabilistic approach.” 2020. Web. 27 Nov 2020.

Vancouver:

Babagholami Mohamadabadi B. Unsupervised visual domain adaptation: a probabilistic approach. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Nov 27]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62650/.

Council of Science Editors:

Babagholami Mohamadabadi B. Unsupervised visual domain adaptation: a probabilistic approach. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62650/


University of Technology, Sydney

5. Zuo, Hua. Transfer learning in Takagi-Sugeno fuzzy models.

Degree: 2018, University of Technology, Sydney

 In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data… (more)

Subjects/Keywords: Machine learning.; Heterogeneous transfer learning.; Fuzzy domain adaptation.; Heterogeneous domain adaptation method.

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

APA (6th Edition):

Zuo, H. (2018). Transfer learning in Takagi-Sugeno fuzzy models. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/127891

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):

Zuo, Hua. “Transfer learning in Takagi-Sugeno fuzzy models.” 2018. Thesis, University of Technology, Sydney. Accessed November 27, 2020. http://hdl.handle.net/10453/127891.

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

MLA Handbook (7th Edition):

Zuo, Hua. “Transfer learning in Takagi-Sugeno fuzzy models.” 2018. Web. 27 Nov 2020.

Vancouver:

Zuo H. Transfer learning in Takagi-Sugeno fuzzy models. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10453/127891.

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

Council of Science Editors:

Zuo H. Transfer learning in Takagi-Sugeno fuzzy models. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/127891

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


Rochester Institute of Technology

6. Alawad, Mohammed Abdullatif. Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain.

Degree: PhD, PhD Program in Computing and Information Sciences, 2019, Rochester Institute of Technology

  A primary factor for the success of machine learning is the quality of labeled training data. However, in many fields, labeled data can be… (more)

Subjects/Keywords: Cardiac electrophysiology; Cardiovascular; Computer simulation; Domain adaptation; Domain generalization; Machine learning

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

APA (6th Edition):

Alawad, M. A. (2019). Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10105

Chicago Manual of Style (16th Edition):

Alawad, Mohammed Abdullatif. “Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed November 27, 2020. https://scholarworks.rit.edu/theses/10105.

MLA Handbook (7th Edition):

Alawad, Mohammed Abdullatif. “Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain.” 2019. Web. 27 Nov 2020.

Vancouver:

Alawad MA. Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2020 Nov 27]. Available from: https://scholarworks.rit.edu/theses/10105.

Council of Science Editors:

Alawad MA. Transferring Generalized Knowledge from Physics-based Simulation to Clinical Domain. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10105


Duke University

7. Li, Yitong. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .

Degree: 2020, Duke University

  Deep learning methods have shown unparalleled performance when trained on vast amounts of diverse labeled training data, often collected at great cost. In many… (more)

Subjects/Keywords: Computer science; domain adaptation; domain generalization; EEG/LFP; transfer learning

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

APA (6th Edition):

Li, Y. (2020). Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/20864

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):

Li, Yitong. “Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .” 2020. Thesis, Duke University. Accessed November 27, 2020. http://hdl.handle.net/10161/20864.

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

MLA Handbook (7th Edition):

Li, Yitong. “Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals .” 2020. Web. 27 Nov 2020.

Vancouver:

Li Y. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . [Internet] [Thesis]. Duke University; 2020. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10161/20864.

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

Council of Science Editors:

Li Y. Learning to Transfer Knowledge from Multiple Sources of Electrophysiological Signals . [Thesis]. Duke University; 2020. Available from: http://hdl.handle.net/10161/20864

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


Tampere University

8. Senhaji, Ali. Incremental Multi-Domain Learning with Domain-Specific Early Exits .

Degree: 2020, Tampere University

 Deep learning architectures can achieve state-of-the-art results in several computer vision tasks. However, these methods are highly specialized, i.e., for every task from a new… (more)

Subjects/Keywords: multi-domain learning ; early exits ; domain adaptation ; multi-task learning

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APA (6th Edition):

Senhaji, A. (2020). Incremental Multi-Domain Learning with Domain-Specific Early Exits . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/121513

Chicago Manual of Style (16th Edition):

Senhaji, Ali. “Incremental Multi-Domain Learning with Domain-Specific Early Exits .” 2020. Masters Thesis, Tampere University. Accessed November 27, 2020. https://trepo.tuni.fi/handle/10024/121513.

MLA Handbook (7th Edition):

Senhaji, Ali. “Incremental Multi-Domain Learning with Domain-Specific Early Exits .” 2020. Web. 27 Nov 2020.

Vancouver:

Senhaji A. Incremental Multi-Domain Learning with Domain-Specific Early Exits . [Internet] [Masters thesis]. Tampere University; 2020. [cited 2020 Nov 27]. Available from: https://trepo.tuni.fi/handle/10024/121513.

Council of Science Editors:

Senhaji A. Incremental Multi-Domain Learning with Domain-Specific Early Exits . [Masters Thesis]. Tampere University; 2020. Available from: https://trepo.tuni.fi/handle/10024/121513


Delft University of Technology

9. Razoux Schultz, Lex (author). Distance Based Source Domain Selection for Automated Sentiment Classification.

Degree: 2018, Delft University of Technology

Automated Sentiment Classification (SC) on short text fragments has been an upcoming field of research. Different machine learning techniques and word representation models have proven… (more)

Subjects/Keywords: sentiment analysis; sentiment classification; domain adaptation; source selection; domain selection

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APA (6th Edition):

Razoux Schultz, L. (. (2018). Distance Based Source Domain Selection for Automated Sentiment Classification. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:bc430a45-3377-40de-9408-428b39b4f196

Chicago Manual of Style (16th Edition):

Razoux Schultz, Lex (author). “Distance Based Source Domain Selection for Automated Sentiment Classification.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:bc430a45-3377-40de-9408-428b39b4f196.

MLA Handbook (7th Edition):

Razoux Schultz, Lex (author). “Distance Based Source Domain Selection for Automated Sentiment Classification.” 2018. Web. 27 Nov 2020.

Vancouver:

Razoux Schultz L(. Distance Based Source Domain Selection for Automated Sentiment Classification. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:bc430a45-3377-40de-9408-428b39b4f196.

Council of Science Editors:

Razoux Schultz L(. Distance Based Source Domain Selection for Automated Sentiment Classification. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:bc430a45-3377-40de-9408-428b39b4f196


University of Texas – Austin

10. -3407-5185. Building effective representations for domain adaptation in coreference resolution.

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

 Over the past few years, research in coreference resolution, one of the core tasks in Natural Language processing, has displayed significant improvement. However, the field… (more)

Subjects/Keywords: Domain adaptation; Domain adversarial; Coreference resolution; Wikipedia; Information extraction; Natural language processing; Neural network

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

APA (6th Edition):

-3407-5185. (2018). Building effective representations for domain adaptation in coreference resolution. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68211

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

Chicago Manual of Style (16th Edition):

-3407-5185. “Building effective representations for domain adaptation in coreference resolution.” 2018. Masters Thesis, University of Texas – Austin. Accessed November 27, 2020. http://hdl.handle.net/2152/68211.

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

MLA Handbook (7th Edition):

-3407-5185. “Building effective representations for domain adaptation in coreference resolution.” 2018. Web. 27 Nov 2020.

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

Vancouver:

-3407-5185. Building effective representations for domain adaptation in coreference resolution. [Internet] [Masters thesis]. University of Texas – Austin; 2018. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/2152/68211.

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

Council of Science Editors:

-3407-5185. Building effective representations for domain adaptation in coreference resolution. [Masters Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68211

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


Université Paris-Sud – Paris XI

11. Marchand, Morgane. Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words.

Degree: Docteur es, Informatique, 2015, Université Paris-Sud – Paris XI

Cette thèse s’intéresse à l’adaptation d’un classifieur statistique d’opinion au niveau du texte d’un domaine à un autre. Cependant, nous exprimons notre opinion différemment selon… (more)

Subjects/Keywords: Fouille d'opinion; Adaptation au domaine; Mots multi-polaires; Opinion mining; Domain adaptation; Multi-polarity words

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APA (6th Edition):

Marchand, M. (2015). Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2015PA112026

Chicago Manual of Style (16th Edition):

Marchand, Morgane. “Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words.” 2015. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed November 27, 2020. http://www.theses.fr/2015PA112026.

MLA Handbook (7th Edition):

Marchand, Morgane. “Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words.” 2015. Web. 27 Nov 2020.

Vancouver:

Marchand M. Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2015. [cited 2020 Nov 27]. Available from: http://www.theses.fr/2015PA112026.

Council of Science Editors:

Marchand M. Domaines et fouille d'opinion : une étude des marqueurs multi-polaires au niveau du texte : Domain Adaptation for Opinion Mining : A Study of Multi-polarity Words. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2015. Available from: http://www.theses.fr/2015PA112026


Temple University

12. Huang, Fei. Improving NLP Systems Using Unconventional, Freely-Available Data.

Degree: PhD, 2013, Temple University

Computer and Information Science

Sentence labeling is a type of pattern recognition task that involves the assignment of a categorical label to each member of… (more)

Subjects/Keywords: Computer science;

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

APA (6th Edition):

Huang, F. (2013). Improving NLP Systems Using Unconventional, Freely-Available Data. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,221031

Chicago Manual of Style (16th Edition):

Huang, Fei. “Improving NLP Systems Using Unconventional, Freely-Available Data.” 2013. Doctoral Dissertation, Temple University. Accessed November 27, 2020. http://digital.library.temple.edu/u?/p245801coll10,221031.

MLA Handbook (7th Edition):

Huang, Fei. “Improving NLP Systems Using Unconventional, Freely-Available Data.” 2013. Web. 27 Nov 2020.

Vancouver:

Huang F. Improving NLP Systems Using Unconventional, Freely-Available Data. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2020 Nov 27]. Available from: http://digital.library.temple.edu/u?/p245801coll10,221031.

Council of Science Editors:

Huang F. Improving NLP Systems Using Unconventional, Freely-Available Data. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,221031


Penn State University

13. Raghuram, Jayaram. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.

Degree: 2014, Penn State University

 This dissertation makes contributions towards the following three closely related, important problems in machine learning: {\em 1. Semi-supervised classification, 2. Semi-supervised learning with instance-level constraints,… (more)

Subjects/Keywords: semisupervised classification; semisupervised constraint based learning; classifier domain adaptation; machine learning

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APA (6th Edition):

Raghuram, J. (2014). Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/23319

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):

Raghuram, Jayaram. “Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.” 2014. Thesis, Penn State University. Accessed November 27, 2020. https://submit-etda.libraries.psu.edu/catalog/23319.

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

MLA Handbook (7th Edition):

Raghuram, Jayaram. “Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.” 2014. Web. 27 Nov 2020.

Vancouver:

Raghuram J. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. [Internet] [Thesis]. Penn State University; 2014. [cited 2020 Nov 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/23319.

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

Council of Science Editors:

Raghuram J. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/23319

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


Carnegie Mellon University

14. Zhang, Shanghang. Deep Understanding of Urban Mobility from CityscapeWebcams.

Degree: 2018, Carnegie Mellon University

 Deep understanding of urban mobility is of great significance for many real-world applications, such as urban traffic management and autonomous driving. This thesis develops deep… (more)

Subjects/Keywords: computer vision; deep learning; domain adaptation; traffic counting; urban mobility; webcam

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

APA (6th Edition):

Zhang, S. (2018). Deep Understanding of Urban Mobility from CityscapeWebcams. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/1190

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, Shanghang. “Deep Understanding of Urban Mobility from CityscapeWebcams.” 2018. Thesis, Carnegie Mellon University. Accessed November 27, 2020. http://repository.cmu.edu/dissertations/1190.

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

MLA Handbook (7th Edition):

Zhang, Shanghang. “Deep Understanding of Urban Mobility from CityscapeWebcams.” 2018. Web. 27 Nov 2020.

Vancouver:

Zhang S. Deep Understanding of Urban Mobility from CityscapeWebcams. [Internet] [Thesis]. Carnegie Mellon University; 2018. [cited 2020 Nov 27]. Available from: http://repository.cmu.edu/dissertations/1190.

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

Council of Science Editors:

Zhang S. Deep Understanding of Urban Mobility from CityscapeWebcams. [Thesis]. Carnegie Mellon University; 2018. Available from: http://repository.cmu.edu/dissertations/1190

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


Kansas State University

15. Li, Xukun. Disaster tweet text and image analysis using deep learning approaches.

Degree: PhD, Department of Computer Science, 2020, Kansas State University

 Fast analysis of damage information after a disaster can inform responders and aid agencies, accelerate real-time response, and guide the allocation of resources. Once the… (more)

Subjects/Keywords: Deep Learning; Domain Adaptation; Tweet Classification; Disaster Data Analysis; Damage Localization

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

APA (6th Edition):

Li, X. (2020). Disaster tweet text and image analysis using deep learning approaches. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/40815

Chicago Manual of Style (16th Edition):

Li, Xukun. “Disaster tweet text and image analysis using deep learning approaches.” 2020. Doctoral Dissertation, Kansas State University. Accessed November 27, 2020. http://hdl.handle.net/2097/40815.

MLA Handbook (7th Edition):

Li, Xukun. “Disaster tweet text and image analysis using deep learning approaches.” 2020. Web. 27 Nov 2020.

Vancouver:

Li X. Disaster tweet text and image analysis using deep learning approaches. [Internet] [Doctoral dissertation]. Kansas State University; 2020. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/2097/40815.

Council of Science Editors:

Li X. Disaster tweet text and image analysis using deep learning approaches. [Doctoral Dissertation]. Kansas State University; 2020. Available from: http://hdl.handle.net/2097/40815


Linköping University

16. Myllylä, Johannes Palm. Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data.

Degree: Computer and Information Science, 2019, Linköping University

  Identifying semantic relations in natural language text is an important component of many knowledge extraction systems. This thesis studies the task of hypernym discovery,… (more)

Subjects/Keywords: NLP; natural language processing; domain adaptation; hypernym; hyponym; Computer Engineering; Datorteknik

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

APA (6th Edition):

Myllylä, J. P. (2019). Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693

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):

Myllylä, Johannes Palm. “Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data.” 2019. Thesis, Linköping University. Accessed November 27, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693.

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

MLA Handbook (7th Edition):

Myllylä, Johannes Palm. “Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data.” 2019. Web. 27 Nov 2020.

Vancouver:

Myllylä JP. Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data. [Internet] [Thesis]. Linköping University; 2019. [cited 2020 Nov 27]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693.

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

Council of Science Editors:

Myllylä JP. Domain Adaptation for Hypernym Discovery via Automatic Collection of Domain-Specific Training Data. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157693

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


Delft University of Technology

17. Li, Jiahui (author). Attention-Aware Age-Agnostic Visual Place Recognition.

Degree: 2019, Delft University of Technology

 A cross-domain visual place recognition (VPR) task is proposed in this work, i.e., matching images of the same architectures depicted in different domains. VPR is… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Attention Mechanism

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

APA (6th Edition):

Li, J. (. (2019). Attention-Aware Age-Agnostic Visual Place Recognition. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7

Chicago Manual of Style (16th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

MLA Handbook (7th Edition):

Li, Jiahui (author). “Attention-Aware Age-Agnostic Visual Place Recognition.” 2019. Web. 27 Nov 2020.

Vancouver:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7.

Council of Science Editors:

Li J(. Attention-Aware Age-Agnostic Visual Place Recognition. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:250d37a9-bc0d-4f8f-8d1a-d31a98dc22d7


Delft University of Technology

18. Lengyel, Attila (author). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.

Degree: 2019, Delft University of Technology

 This work investigates how prior knowledge from physics-based reflection models can be used to improve the performance of semantic segmentation models under an illumination-based domain(more)

Subjects/Keywords: Semantic segmentation; color invariants; deep learning; computer vision; domain adaptation

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

APA (6th Edition):

Lengyel, A. (. (2019). Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a

Chicago Manual of Style (16th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

MLA Handbook (7th Edition):

Lengyel, Attila (author). “Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach.” 2019. Web. 27 Nov 2020.

Vancouver:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a.

Council of Science Editors:

Lengyel A(. Addressing Illumination-Based Domain Shifts in Deep Learning: A Physics-Based Approach. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:f8619273-0e7e-42e3-990b-67e2f6edc78a


Delft University of Technology

19. Naseri Jahfari, Arman (author). Domain Adaptation in Acoustic Rainfall Sensors.

Degree: 2019, Delft University of Technology

 Rainfall is increasing in frequency and intensity due to climate change. Hydrological models exist that can report bottlenecks in urban infrastructures. However, these require accurate… (more)

Subjects/Keywords: Domain Adaptation; Pattern Recognition; Rainfall estimation; Acoustic Sensor

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

APA (6th Edition):

Naseri Jahfari, A. (. (2019). Domain Adaptation in Acoustic Rainfall Sensors. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a4b79bdf-e15d-49c7-ac50-3cfe0dbe5940

Chicago Manual of Style (16th Edition):

Naseri Jahfari, Arman (author). “Domain Adaptation in Acoustic Rainfall Sensors.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:a4b79bdf-e15d-49c7-ac50-3cfe0dbe5940.

MLA Handbook (7th Edition):

Naseri Jahfari, Arman (author). “Domain Adaptation in Acoustic Rainfall Sensors.” 2019. Web. 27 Nov 2020.

Vancouver:

Naseri Jahfari A(. Domain Adaptation in Acoustic Rainfall Sensors. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:a4b79bdf-e15d-49c7-ac50-3cfe0dbe5940.

Council of Science Editors:

Naseri Jahfari A(. Domain Adaptation in Acoustic Rainfall Sensors. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a4b79bdf-e15d-49c7-ac50-3cfe0dbe5940


Delft University of Technology

20. Liu, Xin (author). Unsupervised Cross Domain Image Matching with Outlier Detection.

Degree: 2018, Delft University of Technology

This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the… (more)

Subjects/Keywords: Computer Vision; Domain Adaptation; Image Matching; Outlier Detection

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

APA (6th Edition):

Liu, X. (. (2018). Unsupervised Cross Domain Image Matching with Outlier Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73

Chicago Manual of Style (16th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

MLA Handbook (7th Edition):

Liu, Xin (author). “Unsupervised Cross Domain Image Matching with Outlier Detection.” 2018. Web. 27 Nov 2020.

Vancouver:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73.

Council of Science Editors:

Liu X(. Unsupervised Cross Domain Image Matching with Outlier Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73


University of Ontario Institute of Technology

21. Towhidi, Afsaneh. Unsupervised brand name extraction using domain adaptation.

Degree: 2019, University of Ontario Institute of Technology

 Business intelligence and analytics is an area of research that analyzes the existing business data to extract the insights needed for a successful business planning.… (more)

Subjects/Keywords: Natural language processing; Named entity recognition; Word embedding; Domain adaptation

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

APA (6th Edition):

Towhidi, A. (2019). Unsupervised brand name extraction using domain adaptation. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/1079

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):

Towhidi, Afsaneh. “Unsupervised brand name extraction using domain adaptation.” 2019. Thesis, University of Ontario Institute of Technology. Accessed November 27, 2020. http://hdl.handle.net/10155/1079.

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

MLA Handbook (7th Edition):

Towhidi, Afsaneh. “Unsupervised brand name extraction using domain adaptation.” 2019. Web. 27 Nov 2020.

Vancouver:

Towhidi A. Unsupervised brand name extraction using domain adaptation. [Internet] [Thesis]. University of Ontario Institute of Technology; 2019. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10155/1079.

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

Council of Science Editors:

Towhidi A. Unsupervised brand name extraction using domain adaptation. [Thesis]. University of Ontario Institute of Technology; 2019. Available from: http://hdl.handle.net/10155/1079

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

22. McClintick, Kyle W. Training Data Generation Framework For Machine-Learning Based Classifiers.

Degree: MS, 2018, Worcester Polytechnic Institute

  In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications.… (more)

Subjects/Keywords: channel modeling; classification; deep neural networks; machine learning; unsupervised domain adaptation

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APA (6th Edition):

McClintick, K. W. (2018). Training Data Generation Framework For Machine-Learning Based Classifiers. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-121818-231026 ; https://digitalcommons.wpi.edu/etd-theses/1276

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):

McClintick, Kyle W. “Training Data Generation Framework For Machine-Learning Based Classifiers.” 2018. Thesis, Worcester Polytechnic Institute. Accessed November 27, 2020. etd-121818-231026 ; https://digitalcommons.wpi.edu/etd-theses/1276.

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

MLA Handbook (7th Edition):

McClintick, Kyle W. “Training Data Generation Framework For Machine-Learning Based Classifiers.” 2018. Web. 27 Nov 2020.

Vancouver:

McClintick KW. Training Data Generation Framework For Machine-Learning Based Classifiers. [Internet] [Thesis]. Worcester Polytechnic Institute; 2018. [cited 2020 Nov 27]. Available from: etd-121818-231026 ; https://digitalcommons.wpi.edu/etd-theses/1276.

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

Council of Science Editors:

McClintick KW. Training Data Generation Framework For Machine-Learning Based Classifiers. [Thesis]. Worcester Polytechnic Institute; 2018. Available from: etd-121818-231026 ; https://digitalcommons.wpi.edu/etd-theses/1276

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


University of Waterloo

23. Dorri, Fatemeh. Adapting Component Analysis.

Degree: 2012, University of Waterloo

 A main problem in machine learning is to predict the response variables of a test set given the training data and its corresponding response variables.… (more)

Subjects/Keywords: Domain adaptation; Kernel embedding; Hilbert-Schmidt Independence Criteria; Dimension reduction

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

APA (6th Edition):

Dorri, F. (2012). Adapting Component Analysis. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6738

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):

Dorri, Fatemeh. “Adapting Component Analysis.” 2012. Thesis, University of Waterloo. Accessed November 27, 2020. http://hdl.handle.net/10012/6738.

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

MLA Handbook (7th Edition):

Dorri, Fatemeh. “Adapting Component Analysis.” 2012. Web. 27 Nov 2020.

Vancouver:

Dorri F. Adapting Component Analysis. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10012/6738.

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

Council of Science Editors:

Dorri F. Adapting Component Analysis. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6738

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

24. Jansson, Erik. Domain Adapted Language Models .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology

 BERT is a recent neural network model that has proven it self amassive leap forward in natural language processing. Due to the tedious training required… (more)

Subjects/Keywords: natural language processing; BERT; transformer; domain adaptation; language model; classification

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

APA (6th Edition):

Jansson, E. (2019). Domain Adapted Language Models . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300390

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):

Jansson, Erik. “Domain Adapted Language Models .” 2019. Thesis, Chalmers University of Technology. Accessed November 27, 2020. http://hdl.handle.net/20.500.12380/300390.

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

MLA Handbook (7th Edition):

Jansson, Erik. “Domain Adapted Language Models .” 2019. Web. 27 Nov 2020.

Vancouver:

Jansson E. Domain Adapted Language Models . [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/20.500.12380/300390.

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

Council of Science Editors:

Jansson E. Domain Adapted Language Models . [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/300390

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


Georgia Tech

25. Chu, Fu-Jen. Improving vision-based robotic manipulation with affordance understanding.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 The objective of the thesis is to improve robotic manipulation via vision-based affordance understanding, which would advance the application of robotics to industrial use cases,… (more)

Subjects/Keywords: Robotics; Vision; Deep learning; Affordance; Domain adaptation; Grasp; Grasping; Synthetic data

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

APA (6th Edition):

Chu, F. (2020). Improving vision-based robotic manipulation with affordance understanding. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63687

Chicago Manual of Style (16th Edition):

Chu, Fu-Jen. “Improving vision-based robotic manipulation with affordance understanding.” 2020. Doctoral Dissertation, Georgia Tech. Accessed November 27, 2020. http://hdl.handle.net/1853/63687.

MLA Handbook (7th Edition):

Chu, Fu-Jen. “Improving vision-based robotic manipulation with affordance understanding.” 2020. Web. 27 Nov 2020.

Vancouver:

Chu F. Improving vision-based robotic manipulation with affordance understanding. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/1853/63687.

Council of Science Editors:

Chu F. Improving vision-based robotic manipulation with affordance understanding. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63687

26. Peyrache, Jean-Philippe. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.

Degree: Docteur es, Informatique, 2014, Saint-Etienne

Ces dernières années, l’intérêt pour l’apprentissage automatique n’a cessé d’augmenter dans des domaines aussi variés que la reconnaissance d’images ou l’analyse de données médicales. Cependant,… (more)

Subjects/Keywords: Adaptation de domaine; Apprentissage automatique; Apprentissage semi-supervisé; Apprentissage par transfert; Domain adaptation; Machine learning; Semi-supervised learning; Learning by transfer

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

APA (6th Edition):

Peyrache, J. (2014). Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. (Doctoral Dissertation). Saint-Etienne. Retrieved from http://www.theses.fr/2014STET4023

Chicago Manual of Style (16th Edition):

Peyrache, Jean-Philippe. “Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.” 2014. Doctoral Dissertation, Saint-Etienne. Accessed November 27, 2020. http://www.theses.fr/2014STET4023.

MLA Handbook (7th Edition):

Peyrache, Jean-Philippe. “Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation.” 2014. Web. 27 Nov 2020.

Vancouver:

Peyrache J. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. [Internet] [Doctoral dissertation]. Saint-Etienne; 2014. [cited 2020 Nov 27]. Available from: http://www.theses.fr/2014STET4023.

Council of Science Editors:

Peyrache J. Nouvelles approches itératives avec garanties théoriques pour l'adaptation de domaine non supervisée : New iterative approaches with theoretical guarantees for unsupervised domain adaptation. [Doctoral Dissertation]. Saint-Etienne; 2014. Available from: http://www.theses.fr/2014STET4023


Penn State University

27. Sawant, Neela Kamlakar. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.

Degree: 2013, Penn State University

 Computer vision is an integral aspect of cognitive computing with diverse applications in medical diagnostics and health-care, communication, transportation, entertainment, and data management. It enables… (more)

Subjects/Keywords: statistical modeling; image annotation; ARTEMIS; instance-weighting; mixture models; transfer learning; domain adaptation

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

APA (6th Edition):

Sawant, N. K. (2013). Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/18875

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):

Sawant, Neela Kamlakar. “Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.” 2013. Thesis, Penn State University. Accessed November 27, 2020. https://submit-etda.libraries.psu.edu/catalog/18875.

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

MLA Handbook (7th Edition):

Sawant, Neela Kamlakar. “Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets.” 2013. Web. 27 Nov 2020.

Vancouver:

Sawant NK. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. [Internet] [Thesis]. Penn State University; 2013. [cited 2020 Nov 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/18875.

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

Council of Science Editors:

Sawant NK. Statistical Modeling of Image Semantics from Imperfectly Labeled Data Sets. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/18875

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


Universidade do Rio Grande do Sul

28. Laranjeira, Bruno Rezende. On the application of focused crawling for statistical machine translation domain adaptation.

Degree: 2015, Universidade do Rio Grande do Sul

Statistical Machine Translation (SMT) is highly dependent on the availability of parallel corpora for training. However, these kinds of resource may be hard to be… (more)

Subjects/Keywords: Focused crawling; Linguística computacional; Statistical machine translation; Estatística aplicada; Domain adaptation; Comparable corpora

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

APA (6th Edition):

Laranjeira, B. R. (2015). On the application of focused crawling for statistical machine translation domain adaptation. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/117259

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):

Laranjeira, Bruno Rezende. “On the application of focused crawling for statistical machine translation domain adaptation.” 2015. Thesis, Universidade do Rio Grande do Sul. Accessed November 27, 2020. http://hdl.handle.net/10183/117259.

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

MLA Handbook (7th Edition):

Laranjeira, Bruno Rezende. “On the application of focused crawling for statistical machine translation domain adaptation.” 2015. Web. 27 Nov 2020.

Vancouver:

Laranjeira BR. On the application of focused crawling for statistical machine translation domain adaptation. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2015. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10183/117259.

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

Council of Science Editors:

Laranjeira BR. On the application of focused crawling for statistical machine translation domain adaptation. [Thesis]. Universidade do Rio Grande do Sul; 2015. Available from: http://hdl.handle.net/10183/117259

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


University of Colorado

29. Dligach, Dmitriy. High-performance Word Sense Disambiguation with Less Manual Effort.

Degree: PhD, Computer Science, 2010, University of Colorado

  Supervised learning is a widely used paradigm in Natural Language Processing. This paradigm involves learning a classifier from annotated examples and applying it to… (more)

Subjects/Keywords: active learning; annotation science; domain adaptation; language modeling; semantic features; word sense disambiguation; Computer Sciences

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

APA (6th Edition):

Dligach, D. (2010). High-performance Word Sense Disambiguation with Less Manual Effort. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/15

Chicago Manual of Style (16th Edition):

Dligach, Dmitriy. “High-performance Word Sense Disambiguation with Less Manual Effort.” 2010. Doctoral Dissertation, University of Colorado. Accessed November 27, 2020. https://scholar.colorado.edu/csci_gradetds/15.

MLA Handbook (7th Edition):

Dligach, Dmitriy. “High-performance Word Sense Disambiguation with Less Manual Effort.” 2010. Web. 27 Nov 2020.

Vancouver:

Dligach D. High-performance Word Sense Disambiguation with Less Manual Effort. [Internet] [Doctoral dissertation]. University of Colorado; 2010. [cited 2020 Nov 27]. Available from: https://scholar.colorado.edu/csci_gradetds/15.

Council of Science Editors:

Dligach D. High-performance Word Sense Disambiguation with Less Manual Effort. [Doctoral Dissertation]. University of Colorado; 2010. Available from: https://scholar.colorado.edu/csci_gradetds/15


Robert Gordon University

30. Muhammad, Aminu. Contextual lexicon-based sentiment analysis for social media.

Degree: PhD, 2016, Robert Gordon University

 Sentiment analysis concerns the computational study of opinions expressed in text. Social media domains provide a wealth of opinionated data, thus, creating a greater need… (more)

Subjects/Keywords: 302.23; Sentiment analysis; SentiWordNet; Contextual analysis; Domain adaptation; Hybrid sentiment lexicon; Distant supervision; Emotion features

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

APA (6th Edition):

Muhammad, A. (2016). Contextual lexicon-based sentiment analysis for social media. (Doctoral Dissertation). Robert Gordon University. Retrieved from http://hdl.handle.net/10059/1571

Chicago Manual of Style (16th Edition):

Muhammad, Aminu. “Contextual lexicon-based sentiment analysis for social media.” 2016. Doctoral Dissertation, Robert Gordon University. Accessed November 27, 2020. http://hdl.handle.net/10059/1571.

MLA Handbook (7th Edition):

Muhammad, Aminu. “Contextual lexicon-based sentiment analysis for social media.” 2016. Web. 27 Nov 2020.

Vancouver:

Muhammad A. Contextual lexicon-based sentiment analysis for social media. [Internet] [Doctoral dissertation]. Robert Gordon University; 2016. [cited 2020 Nov 27]. Available from: http://hdl.handle.net/10059/1571.

Council of Science Editors:

Muhammad A. Contextual lexicon-based sentiment analysis for social media. [Doctoral Dissertation]. Robert Gordon University; 2016. Available from: http://hdl.handle.net/10059/1571

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