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You searched for subject:(unlabeled data). Showing records 1 – 7 of 7 total matches.

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University of Cincinnati

1. Shi, Zhe. Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management.

Degree: PhD, Engineering and Applied Science: Mechanical Engineering, 2018, University of Cincinnati

 Advances in data acquisition and storage technologies have enabled the easy accumulation of a large amount of training data from many real-world applications in industry.… (more)

Subjects/Keywords: Mechanical Engineering; Regression; Classification; PHM; Semi-supervised Ensemble Learning; Unlabeled samples; Insufficient training data set

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

APA (6th Edition):

Shi, Z. (2018). Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420632837268

Chicago Manual of Style (16th Edition):

Shi, Zhe. “Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management.” 2018. Doctoral Dissertation, University of Cincinnati. Accessed January 26, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420632837268.

MLA Handbook (7th Edition):

Shi, Zhe. “Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management.” 2018. Web. 26 Jan 2020.

Vancouver:

Shi Z. Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management. [Internet] [Doctoral dissertation]. University of Cincinnati; 2018. [cited 2020 Jan 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420632837268.

Council of Science Editors:

Shi Z. Semi-supervised Ensemble Learning Methods for Enhanced Prognostics and Health Management. [Doctoral Dissertation]. University of Cincinnati; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1522420632837268

2. Urner, Ruth. Learning with non-Standard Supervision.

Degree: 2013, University of Waterloo

 Machine learning has enjoyed astounding practical success in a wide range of applications in recent years-practical success that often hurries ahead of our theoretical understanding.… (more)

Subjects/Keywords: Machine learning theory; Sample complexity; Unlabeled data

…training data available only from a different (but related) task. Unlabeled training… …practical applications unlabeled data has been successfully utilized to boost up learning… …availability of unlabeled training data beneficial? When can the access to unlabeled data provably… …from unlabeled data? In this thesis, we analyze three settings of learning with non-standard… …valuable information from unlabeled data for classification tasks. Thus, introducing PL to the… 

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APA (6th Edition):

Urner, R. (2013). Learning with non-Standard Supervision. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7925

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):

Urner, Ruth. “Learning with non-Standard Supervision.” 2013. Thesis, University of Waterloo. Accessed January 26, 2020. http://hdl.handle.net/10012/7925.

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

MLA Handbook (7th Edition):

Urner, Ruth. “Learning with non-Standard Supervision.” 2013. Web. 26 Jan 2020.

Vancouver:

Urner R. Learning with non-Standard Supervision. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/10012/7925.

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

Council of Science Editors:

Urner R. Learning with non-Standard Supervision. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7925

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

3. Bocancea, Andreea. Supervised Classification Leveraging Refined Unlabeled Data.

Degree: Faculty of Science & Engineering, 2015, Linköping UniversityLinköping University

  This thesis focuses on how unlabeled data can improve supervised learning classi-fiers in all contexts, for both scarce to abundant label situations. This is… (more)

Subjects/Keywords: Semi-supervised learning; supervised; active learning; unlabeled; TSVM; graph-based; Computer and Information Sciences; Data- och informationsvetenskap

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

APA (6th Edition):

Bocancea, A. (2015). Supervised Classification Leveraging Refined Unlabeled Data. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119320

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):

Bocancea, Andreea. “Supervised Classification Leveraging Refined Unlabeled Data.” 2015. Thesis, Linköping UniversityLinköping University. Accessed January 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119320.

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

MLA Handbook (7th Edition):

Bocancea, Andreea. “Supervised Classification Leveraging Refined Unlabeled Data.” 2015. Web. 26 Jan 2020.

Vancouver:

Bocancea A. Supervised Classification Leveraging Refined Unlabeled Data. [Internet] [Thesis]. Linköping UniversityLinköping University; 2015. [cited 2020 Jan 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119320.

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

Council of Science Editors:

Bocancea A. Supervised Classification Leveraging Refined Unlabeled Data. [Thesis]. Linköping UniversityLinköping University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119320

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

4. WANG GANG. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.

Degree: 2009, National University of Singapore

Subjects/Keywords: Domain Knowledge; Unlabeled Data; Text Semantics; Multi-resolution analysis; Transductive Learning; Bootstrapping.

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

APA (6th Edition):

GANG, W. (2009). A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/15829

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):

GANG, WANG. “A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.” 2009. Thesis, National University of Singapore. Accessed January 26, 2020. http://scholarbank.nus.edu.sg/handle/10635/15829.

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

MLA Handbook (7th Edition):

GANG, WANG. “A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video.” 2009. Web. 26 Jan 2020.

Vancouver:

GANG W. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. [Internet] [Thesis]. National University of Singapore; 2009. [cited 2020 Jan 26]. Available from: http://scholarbank.nus.edu.sg/handle/10635/15829.

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

Council of Science Editors:

GANG W. A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video. [Thesis]. National University of Singapore; 2009. Available from: http://scholarbank.nus.edu.sg/handle/10635/15829

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


University of Queensland

5. Chen, Ling. Healthcare data mining from multi-source data.

Degree: School of Information Technology and Electrical Engineering, 2017, University of Queensland

 The "big data" challenge is changing the way we acquire, store, analyse, and draw conclusions from data. How we effectively and efficiently "mine" the data(more)

Subjects/Keywords: Personal health index mining; Health examination records; Classification with label uncertainty; Classification with large unlabeled data; Graph-based semi-supervised learning; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems

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

APA (6th Edition):

Chen, L. (2017). Healthcare data mining from multi-source data. (Thesis). University of Queensland. Retrieved from http://espace.library.uq.edu.au/view/UQ:450815

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

Chicago Manual of Style (16th Edition):

Chen, Ling. “Healthcare data mining from multi-source data.” 2017. Thesis, University of Queensland. Accessed January 26, 2020. http://espace.library.uq.edu.au/view/UQ:450815.

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

MLA Handbook (7th Edition):

Chen, Ling. “Healthcare data mining from multi-source data.” 2017. Web. 26 Jan 2020.

Vancouver:

Chen L. Healthcare data mining from multi-source data. [Internet] [Thesis]. University of Queensland; 2017. [cited 2020 Jan 26]. Available from: http://espace.library.uq.edu.au/view/UQ:450815.

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

Council of Science Editors:

Chen L. Healthcare data mining from multi-source data. [Thesis]. University of Queensland; 2017. Available from: http://espace.library.uq.edu.au/view/UQ:450815

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

6. Chang, Shiyu. Similarity learning in the era of big data.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 This dissertation studies the problem of similarity learning in the era of big data with heavy emphasis on real-world applications in social media. As in… (more)

Subjects/Keywords: Similarity Learning; Big Data; Large Volume Data; Multimodality Data; High-velocity Data; Large-scale; Supervised Similarity Learning; Network Embedding; Deep Embedding; Heterogeneous Network; Streaming Network; Positive-Unlabeled Learning; Link Prediction; Recommendation; Social Media; Search and Retrieval

data. The weights, denoted by the gray scale, managed to deemphasize these unlabeled data… …available and the vast majority of data remain unlabeled, which has significant impact on… …help of unlabeled data, which is copiously available. For example, Hinton and Salakhutdinov… …also include positive ones [97]) from unlabeled data, and 2) applying… …classifier with the unlabeled data interpreted as weighted negative samples [99, 100]… 

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APA (6th Edition):

Chang, S. (2016). Similarity learning in the era of big data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95303

Chicago Manual of Style (16th Edition):

Chang, Shiyu. “Similarity learning in the era of big data.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 26, 2020. http://hdl.handle.net/2142/95303.

MLA Handbook (7th Edition):

Chang, Shiyu. “Similarity learning in the era of big data.” 2016. Web. 26 Jan 2020.

Vancouver:

Chang S. Similarity learning in the era of big data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/2142/95303.

Council of Science Editors:

Chang S. Similarity learning in the era of big data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95303

7. Patel, Jiten. Enhanced classification approach with semi-supervised learning for reliability-based system design.

Degree: PhD, Mechanical Engineering, 2012, Georgia Tech

 Traditionally design engineers have used the Factor of Safety method for ensuring that designs do not fail in the field. Access to advanced computational tools… (more)

Subjects/Keywords: Labeled and unlabeled data; Semi-supervised learning; Probability of failure; Structural reliability; System design; Classification; Safety factor in engineering; Reliability (Engineering); Surrogate-based optimization; Supervised learning (Machine learning); Expectation-maximization algorithms

…of SSL-2 as Number of Unlabeled Data is Changed 175 5.2.4 Performance of SSL-2 as Number… …27 Table 3. 1Expectation Maximization Algorithm for Clustering of Unlabeled data.. …..131… …116 Figure 3. 13 Unlabeled data is separated into two constituent Gaussian components after… …173 Figure 5. 5 Number of misclassifications with SSL-2 as no. of unlabeled data is varied… …of unlabeled data points µ = Mean vector γ = Posterior probability Z = Number of… 

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

APA (6th Edition):

Patel, J. (2012). Enhanced classification approach with semi-supervised learning for reliability-based system design. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44872

Chicago Manual of Style (16th Edition):

Patel, Jiten. “Enhanced classification approach with semi-supervised learning for reliability-based system design.” 2012. Doctoral Dissertation, Georgia Tech. Accessed January 26, 2020. http://hdl.handle.net/1853/44872.

MLA Handbook (7th Edition):

Patel, Jiten. “Enhanced classification approach with semi-supervised learning for reliability-based system design.” 2012. Web. 26 Jan 2020.

Vancouver:

Patel J. Enhanced classification approach with semi-supervised learning for reliability-based system design. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2020 Jan 26]. Available from: http://hdl.handle.net/1853/44872.

Council of Science Editors:

Patel J. Enhanced classification approach with semi-supervised learning for reliability-based system design. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44872

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