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You searched for subject:(Metric learning). Showing records 1 – 30 of 59 total matches.

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1. Mochihashi, Daichi. Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ.

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

Subjects/Keywords: Metric Learning

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

APA (6th Edition):

Mochihashi, D. (n.d.). Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ. (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/10061/2837

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):

Mochihashi, Daichi. “Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed April 24, 2019. http://hdl.handle.net/10061/2837.

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):

Mochihashi, Daichi. “Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ.” Web. 24 Apr 2019.

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

Vancouver:

Mochihashi D. Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10061/2837.

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:

Mochihashi D. Distributional Approaches to Natural Language Processing : 自然言語処理への分布的アプローチ; シゼン ゲンゴ ショリ エノ ブンプテキ アプローチ. [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/10061/2837

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


Georgia State University

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhu, Yun. “Dynamic Ensemble Selection with Regional Expertise.” 2015. Doctoral Dissertation, Georgia State University. Accessed April 24, 2019. https://scholarworks.gsu.edu/cs_diss/97.

MLA Handbook (7th Edition):

Zhu, Yun. “Dynamic Ensemble Selection with Regional Expertise.” 2015. Web. 24 Apr 2019.

Vancouver:

Zhu Y. Dynamic Ensemble Selection with Regional Expertise. [Internet] [Doctoral dissertation]. Georgia State University; 2015. [cited 2019 Apr 24]. Available from: https://scholarworks.gsu.edu/cs_diss/97.

Council of Science Editors:

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


University of New South Wales

3. Ghanavati, Mojgan. Learning from imbalanced and heterogeneous data.

Degree: Computer Science & Engineering, 2017, University of New South Wales

 Big data analysis is a term describing the analysis of large and/or complex datasets using a series of techniques including but not limited to machine… (more)

Subjects/Keywords: Imbalanced; Metric Learning; Classification

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APA (6th Edition):

Ghanavati, M. (2017). Learning from imbalanced and heterogeneous data. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/57827 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:44955/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Ghanavati, Mojgan. “Learning from imbalanced and heterogeneous data.” 2017. Doctoral Dissertation, University of New South Wales. Accessed April 24, 2019. http://handle.unsw.edu.au/1959.4/57827 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:44955/SOURCE02?view=true.

MLA Handbook (7th Edition):

Ghanavati, Mojgan. “Learning from imbalanced and heterogeneous data.” 2017. Web. 24 Apr 2019.

Vancouver:

Ghanavati M. Learning from imbalanced and heterogeneous data. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2019 Apr 24]. Available from: http://handle.unsw.edu.au/1959.4/57827 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:44955/SOURCE02?view=true.

Council of Science Editors:

Ghanavati M. Learning from imbalanced and heterogeneous data. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/57827 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:44955/SOURCE02?view=true


Australian National University

4. Faraki, Masoud. The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning .

Degree: 2018, Australian National University

 A diverse number of tasks in computer vision and machine learning enjoy from representations of data that are compact yet discriminative, informative and robust to… (more)

Subjects/Keywords: Riemannian manifolds; Coding; Metric Learning; Deep learning

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

APA (6th Edition):

Faraki, M. (2018). The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/142557

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):

Faraki, Masoud. “The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning .” 2018. Thesis, Australian National University. Accessed April 24, 2019. http://hdl.handle.net/1885/142557.

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

MLA Handbook (7th Edition):

Faraki, Masoud. “The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning .” 2018. Web. 24 Apr 2019.

Vancouver:

Faraki M. The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning . [Internet] [Thesis]. Australian National University; 2018. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1885/142557.

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

Council of Science Editors:

Faraki M. The Role of Riemannian Manifolds in Computer Vision: From Coding to Deep Metric Learning . [Thesis]. Australian National University; 2018. Available from: http://hdl.handle.net/1885/142557

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


Clemson University

5. Guo, Hanyu. One-shot Learning In Deep Sequential Generative Models.

Degree: MS, Electrical and Computer Engineering (Holcomb Dept. of), 2017, Clemson University

 Regardless of the Deep Learning community's continuous advancements, the challenging domain of one-shot learning still persists. While the human brain is capable of learning a… (more)

Subjects/Keywords: generative model; meta learning; metric learning; one-shot learning; sequential model

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APA (6th Edition):

Guo, H. (2017). One-shot Learning In Deep Sequential Generative Models. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/2792

Chicago Manual of Style (16th Edition):

Guo, Hanyu. “One-shot Learning In Deep Sequential Generative Models.” 2017. Masters Thesis, Clemson University. Accessed April 24, 2019. https://tigerprints.clemson.edu/all_theses/2792.

MLA Handbook (7th Edition):

Guo, Hanyu. “One-shot Learning In Deep Sequential Generative Models.” 2017. Web. 24 Apr 2019.

Vancouver:

Guo H. One-shot Learning In Deep Sequential Generative Models. [Internet] [Masters thesis]. Clemson University; 2017. [cited 2019 Apr 24]. Available from: https://tigerprints.clemson.edu/all_theses/2792.

Council of Science Editors:

Guo H. One-shot Learning In Deep Sequential Generative Models. [Masters Thesis]. Clemson University; 2017. Available from: https://tigerprints.clemson.edu/all_theses/2792


Ohio University

6. Shi, Bibo. Diversification and Generalization for Metric Learning with Applications in Neuroimaging.

Degree: PhD, Computer Science (Engineering and Technology), 2015, Ohio University

 Many machine learning algorithms rely on “good” metrics to quantify the distances or similarities between data instances. Context dependent metrics learned from the training data… (more)

Subjects/Keywords: Computer Science; Machine Learning; Metric Learning; Neuroimaging; Thin-Plate Splines; ADNI

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

APA (6th Edition):

Shi, B. (2015). Diversification and Generalization for Metric Learning with Applications in Neuroimaging. (Doctoral Dissertation). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448980736

Chicago Manual of Style (16th Edition):

Shi, Bibo. “Diversification and Generalization for Metric Learning with Applications in Neuroimaging.” 2015. Doctoral Dissertation, Ohio University. Accessed April 24, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448980736.

MLA Handbook (7th Edition):

Shi, Bibo. “Diversification and Generalization for Metric Learning with Applications in Neuroimaging.” 2015. Web. 24 Apr 2019.

Vancouver:

Shi B. Diversification and Generalization for Metric Learning with Applications in Neuroimaging. [Internet] [Doctoral dissertation]. Ohio University; 2015. [cited 2019 Apr 24]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448980736.

Council of Science Editors:

Shi B. Diversification and Generalization for Metric Learning with Applications in Neuroimaging. [Doctoral Dissertation]. Ohio University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448980736


University of California – San Diego

7. Lim, Daryl Kah Hian. Learning to Rank for Retrieval and Recommendation.

Degree: Electrical Engineering (Intelsys, Robotics and Cont), 2016, University of California – San Diego

 Automated systems which can accurately surface relevant content for a given query have become an indispensable tool for navigating large and complex data collections which… (more)

Subjects/Keywords: Computer science; Electrical engineering; Information Retrieval; Learning to Rank; Machine Learning; Metric Learning; Recommender Systems

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

APA (6th Edition):

Lim, D. K. H. (2016). Learning to Rank for Retrieval and Recommendation. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5bm9g2n1

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):

Lim, Daryl Kah Hian. “Learning to Rank for Retrieval and Recommendation.” 2016. Thesis, University of California – San Diego. Accessed April 24, 2019. http://www.escholarship.org/uc/item/5bm9g2n1.

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

MLA Handbook (7th Edition):

Lim, Daryl Kah Hian. “Learning to Rank for Retrieval and Recommendation.” 2016. Web. 24 Apr 2019.

Vancouver:

Lim DKH. Learning to Rank for Retrieval and Recommendation. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Apr 24]. Available from: http://www.escholarship.org/uc/item/5bm9g2n1.

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

Council of Science Editors:

Lim DKH. Learning to Rank for Retrieval and Recommendation. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/5bm9g2n1

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


University of Otago

8. Sallis, Adrian James. A software metric suite for evaluating the usability of Learning Management Systems .

Degree: 2011, University of Otago

 In the last decade the World Wide Web has grown rapidly in both acceptance and application across a wide range of business, scientific, educational and… (more)

Subjects/Keywords: Usability; Internet; cost-effective; metric suite; Learning Management Systems

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APA (6th Edition):

Sallis, A. J. (2011). A software metric suite for evaluating the usability of Learning Management Systems . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/1282

Chicago Manual of Style (16th Edition):

Sallis, Adrian James. “A software metric suite for evaluating the usability of Learning Management Systems .” 2011. Masters Thesis, University of Otago. Accessed April 24, 2019. http://hdl.handle.net/10523/1282.

MLA Handbook (7th Edition):

Sallis, Adrian James. “A software metric suite for evaluating the usability of Learning Management Systems .” 2011. Web. 24 Apr 2019.

Vancouver:

Sallis AJ. A software metric suite for evaluating the usability of Learning Management Systems . [Internet] [Masters thesis]. University of Otago; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/10523/1282.

Council of Science Editors:

Sallis AJ. A software metric suite for evaluating the usability of Learning Management Systems . [Masters Thesis]. University of Otago; 2011. Available from: http://hdl.handle.net/10523/1282


Université Catholique de Louvain

9. Deplasse, Victor. Metric learning for efficient search with high-dimensional multi-modal data.

Degree: 2017, Université Catholique de Louvain

Many machine learning algorithms are based on the similarity or distance between objects. For these algorithms, metric learning is a useful preprocessing step to learn… (more)

Subjects/Keywords: Metric learning; Multi-modal data; Similarity; Mahalanobis distance; Million Song Dataset

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APA (6th Edition):

Deplasse, V. (2017). Metric learning for efficient search with high-dimensional multi-modal data. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:10658

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):

Deplasse, Victor. “Metric learning for efficient search with high-dimensional multi-modal data.” 2017. Thesis, Université Catholique de Louvain. Accessed April 24, 2019. http://hdl.handle.net/2078.1/thesis:10658.

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

MLA Handbook (7th Edition):

Deplasse, Victor. “Metric learning for efficient search with high-dimensional multi-modal data.” 2017. Web. 24 Apr 2019.

Vancouver:

Deplasse V. Metric learning for efficient search with high-dimensional multi-modal data. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2078.1/thesis:10658.

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

Council of Science Editors:

Deplasse V. Metric learning for efficient search with high-dimensional multi-modal data. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/thesis:10658

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


University of Utah

10. Machanavajhala, Swetha. Accent classification: learning a distrance metric over phonetic strings.

Degree: MS, Computing (School of), 2013, University of Utah

 Presently, speech recognition is gaining worldwide popularity in applications like Google Voice, speech-to-text reporter (speech-to-text transcription, video captioning, real-time transcriptions), hands-free computing, and video games.… (more)

Subjects/Keywords: Accent classification; Distance metric learning; Kernels; Machine learning; Speech recognition; Swetha Machanavajhala

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APA (6th Edition):

Machanavajhala, S. (2013). Accent classification: learning a distrance metric over phonetic strings. (Masters Thesis). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/2617/rec/125

Chicago Manual of Style (16th Edition):

Machanavajhala, Swetha. “Accent classification: learning a distrance metric over phonetic strings.” 2013. Masters Thesis, University of Utah. Accessed April 24, 2019. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/2617/rec/125.

MLA Handbook (7th Edition):

Machanavajhala, Swetha. “Accent classification: learning a distrance metric over phonetic strings.” 2013. Web. 24 Apr 2019.

Vancouver:

Machanavajhala S. Accent classification: learning a distrance metric over phonetic strings. [Internet] [Masters thesis]. University of Utah; 2013. [cited 2019 Apr 24]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/2617/rec/125.

Council of Science Editors:

Machanavajhala S. Accent classification: learning a distrance metric over phonetic strings. [Masters Thesis]. University of Utah; 2013. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/2617/rec/125

11. Michel, Fabrice. Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni.

Degree: Docteur es, Mathématiques appliquées, 2013, Châtenay-Malabry, Ecole centrale de Paris

Alors que la perspective de la fusion d’images médicales capturées par des systèmes d’imageries de type différent est largement contemplée, la mise en pratique est… (more)

Subjects/Keywords: Apprentissage statistique; Recalage déformable; Apprentissage de métrique; Machine-learning; Deformable registration; Metric-learning

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APA (6th Edition):

Michel, F. (2013). Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2013ECAP0055

Chicago Manual of Style (16th Edition):

Michel, Fabrice. “Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni.” 2013. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed April 24, 2019. http://www.theses.fr/2013ECAP0055.

MLA Handbook (7th Edition):

Michel, Fabrice. “Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni.” 2013. Web. 24 Apr 2019.

Vancouver:

Michel F. Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2013ECAP0055.

Council of Science Editors:

Michel F. Multi-modal similarity learning for 3D deformable registration of medical images : Titre français non fourni. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. Available from: http://www.theses.fr/2013ECAP0055


Rochester Institute of Technology

12. Peri, Dheeraj Kumar. Multi-modal learning using deep neural networks.

Degree: MS, Computer Engineering, 2018, Rochester Institute of Technology

  Humans have an incredible ability to process and understand information from multiple sources such as images, video, text, and speech. Recent success of deep… (more)

Subjects/Keywords: Computer vision; Deep learning; Long short term memory; Metric learning; Natural language processing

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

APA (6th Edition):

Peri, D. K. (2018). Multi-modal learning using deep neural networks. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9926

Chicago Manual of Style (16th Edition):

Peri, Dheeraj Kumar. “Multi-modal learning using deep neural networks.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed April 24, 2019. https://scholarworks.rit.edu/theses/9926.

MLA Handbook (7th Edition):

Peri, Dheeraj Kumar. “Multi-modal learning using deep neural networks.” 2018. Web. 24 Apr 2019.

Vancouver:

Peri DK. Multi-modal learning using deep neural networks. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2019 Apr 24]. Available from: https://scholarworks.rit.edu/theses/9926.

Council of Science Editors:

Peri DK. Multi-modal learning using deep neural networks. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9926


University of Texas – Austin

13. Jain, Prateek. Large scale optimization methods for metric and kernel learning.

Degree: Computer Sciences, 2009, University of Texas – Austin

 A large number of machine learning algorithms are critically dependent on the underlying distance/metric/similarity function. Learning an appropriate distance function is therefore crucial to the… (more)

Subjects/Keywords: Rank minimization; Metric learning; Kernel learning; Fast similarity search; Locality sensitive hashing

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APA (6th Edition):

Jain, P. (2009). Large scale optimization methods for metric and kernel learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/27132

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):

Jain, Prateek. “Large scale optimization methods for metric and kernel learning.” 2009. Thesis, University of Texas – Austin. Accessed April 24, 2019. http://hdl.handle.net/2152/27132.

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

MLA Handbook (7th Edition):

Jain, Prateek. “Large scale optimization methods for metric and kernel learning.” 2009. Web. 24 Apr 2019.

Vancouver:

Jain P. Large scale optimization methods for metric and kernel learning. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/2152/27132.

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

Council of Science Editors:

Jain P. Large scale optimization methods for metric and kernel learning. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/27132

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


University of New South Wales

14. Dang, Shaobo. Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data.

Degree: Computer Science & Engineering, 2018, University of New South Wales

 This thesis describes novel approaches to the problem of outlier detection. It is one of the most important problems in the field of machine learning(more)

Subjects/Keywords: Heterogeneous; Outlier Detection; One Class Classification; Metric Learning; Imbalanced Data; Multiple Kernel Learning

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APA (6th Edition):

Dang, S. (2018). Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Dang, Shaobo. “Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data.” 2018. Doctoral Dissertation, University of New South Wales. Accessed April 24, 2019. http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true.

MLA Handbook (7th Edition):

Dang, Shaobo. “Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data.” 2018. Web. 24 Apr 2019.

Vancouver:

Dang S. Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2019 Apr 24]. Available from: http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true.

Council of Science Editors:

Dang S. Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true

15. Nicolae, Maria-Irina. Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes.

Degree: Docteur es, Informatique, 2016, Lyon

La notion de métrique joue un rôle clef dans les problèmes d’apprentissage automatique tels que la classification, le clustering et le ranking. L’apprentissage à partir… (more)

Subjects/Keywords: Apprentissage de métriques; Apprentissage statistique; Théorie de l'apprentissage; Classification; Séries temporelles; Metric learning; Statistical learning; Learning theory; Classification; Time series

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

APA (6th Edition):

Nicolae, M. (2016). Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSES062

Chicago Manual of Style (16th Edition):

Nicolae, Maria-Irina. “Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes.” 2016. Doctoral Dissertation, Lyon. Accessed April 24, 2019. http://www.theses.fr/2016LYSES062.

MLA Handbook (7th Edition):

Nicolae, Maria-Irina. “Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes.” 2016. Web. 24 Apr 2019.

Vancouver:

Nicolae M. Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2016LYSES062.

Council of Science Editors:

Nicolae M. Learning similarities for linear classification : theoretical foundations and algorithms : Apprentissage de similarités pour la classification linéaire : fondements théoriques et algorithmes. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSES062


University of Oxford

16. Choy, Tze Leung. Sparse distance metric learning.

Degree: PhD, 2014, University of Oxford

 A good distance metric can improve the accuracy of a nearest neighbour classifier. Xing et al. (2002) proposed distance metric learning to find a linear… (more)

Subjects/Keywords: 519.5; Statistics (see also social sciences); Pattern recognition (statistics); sparsity; distance metric learning; L1 penalty

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

APA (6th Edition):

Choy, T. L. (2014). Sparse distance metric learning. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:a98695a3-0a60-448f-9ec0-63da3c37f7fa ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635222

Chicago Manual of Style (16th Edition):

Choy, Tze Leung. “Sparse distance metric learning.” 2014. Doctoral Dissertation, University of Oxford. Accessed April 24, 2019. http://ora.ox.ac.uk/objects/uuid:a98695a3-0a60-448f-9ec0-63da3c37f7fa ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635222.

MLA Handbook (7th Edition):

Choy, Tze Leung. “Sparse distance metric learning.” 2014. Web. 24 Apr 2019.

Vancouver:

Choy TL. Sparse distance metric learning. [Internet] [Doctoral dissertation]. University of Oxford; 2014. [cited 2019 Apr 24]. Available from: http://ora.ox.ac.uk/objects/uuid:a98695a3-0a60-448f-9ec0-63da3c37f7fa ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635222.

Council of Science Editors:

Choy TL. Sparse distance metric learning. [Doctoral Dissertation]. University of Oxford; 2014. Available from: http://ora.ox.ac.uk/objects/uuid:a98695a3-0a60-448f-9ec0-63da3c37f7fa ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635222


UCLA

17. Conn, Daniel Joshua. Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions.

Degree: Biostatistics, 2018, UCLA

 When the number of covariates is small, nonparametric regression methods serve a number of useful purposes. In this setting, nonparametric regression methods often demonstrate better… (more)

Subjects/Keywords: Biostatistics; Fuzzy Forests; Kernel Regression; Metric Learning; Nonparametric Estimation; Random Forests; Survival Analysis

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APA (6th Edition):

Conn, D. J. (2018). Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/9z03w0zk

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):

Conn, Daniel Joshua. “Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions.” 2018. Thesis, UCLA. Accessed April 24, 2019. http://www.escholarship.org/uc/item/9z03w0zk.

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

MLA Handbook (7th Edition):

Conn, Daniel Joshua. “Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions.” 2018. Web. 24 Apr 2019.

Vancouver:

Conn DJ. Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions. [Internet] [Thesis]. UCLA; 2018. [cited 2019 Apr 24]. Available from: http://www.escholarship.org/uc/item/9z03w0zk.

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

Council of Science Editors:

Conn DJ. Utilization of Low Dimensional Structure to Improve the Performance of Nonparametric Estimation in High Dimensions. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/9z03w0zk

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


Delft University of Technology

18. Nagaki, K. Reward system design for incorporating control performance:.

Degree: 2015, Delft University of Technology

 Reinforcement learning (RL) is a machine learning technique whereby the controller learns the control law by optimizing the received cumulative amount of reward. A reward… (more)

Subjects/Keywords: Reinforcement Learning; Reward function; Petri net; Metric Interval Temporal Logic; MSc thesis

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

APA (6th Edition):

Nagaki, K. (2015). Reward system design for incorporating control performance:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fb784b70-bad4-46be-adfc-fcf1b013e27f

Chicago Manual of Style (16th Edition):

Nagaki, K. “Reward system design for incorporating control performance:.” 2015. Masters Thesis, Delft University of Technology. Accessed April 24, 2019. http://resolver.tudelft.nl/uuid:fb784b70-bad4-46be-adfc-fcf1b013e27f.

MLA Handbook (7th Edition):

Nagaki, K. “Reward system design for incorporating control performance:.” 2015. Web. 24 Apr 2019.

Vancouver:

Nagaki K. Reward system design for incorporating control performance:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Apr 24]. Available from: http://resolver.tudelft.nl/uuid:fb784b70-bad4-46be-adfc-fcf1b013e27f.

Council of Science Editors:

Nagaki K. Reward system design for incorporating control performance:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:fb784b70-bad4-46be-adfc-fcf1b013e27f


Queensland University of Technology

19. Chen, Zetao. Biologically-inspired place recognition with neural networks.

Degree: 2016, Queensland University of Technology

 This thesis explores two aspects of biologically inspired methods for place recognition, a key component of navigation. The first key theme is to explore the… (more)

Subjects/Keywords: Biologically inspired robotics; Place recognition; Robot localization; Long-term autonomy; SLAM; Convolutional neural network; Deep learning; Grid cells; Metric learning

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

APA (6th Edition):

Chen, Z. (2016). Biologically-inspired place recognition with neural networks. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/98550/

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, Zetao. “Biologically-inspired place recognition with neural networks.” 2016. Thesis, Queensland University of Technology. Accessed April 24, 2019. https://eprints.qut.edu.au/98550/.

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

MLA Handbook (7th Edition):

Chen, Zetao. “Biologically-inspired place recognition with neural networks.” 2016. Web. 24 Apr 2019.

Vancouver:

Chen Z. Biologically-inspired place recognition with neural networks. [Internet] [Thesis]. Queensland University of Technology; 2016. [cited 2019 Apr 24]. Available from: https://eprints.qut.edu.au/98550/.

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

Council of Science Editors:

Chen Z. Biologically-inspired place recognition with neural networks. [Thesis]. Queensland University of Technology; 2016. Available from: https://eprints.qut.edu.au/98550/

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

20. Do, Cao Tri. Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification.

Degree: Docteur es, Mathématiques et Informatique, 2016, Grenoble Alpes

La définition d'une métrique entre des séries temporelles est un élément important pour de nombreuses tâches en analyse ou en fouille de données, tel que… (more)

Subjects/Keywords: Apprentissage statistique; Séries temporelles; Apprentissage de métrique; Classification; Svm; Knn; Machine Learning; Time Series; Metric learning; Classification; Svm; Knn; 004; 510

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

APA (6th Edition):

Do, C. T. (2016). Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2016GREAM028

Chicago Manual of Style (16th Edition):

Do, Cao Tri. “Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification.” 2016. Doctoral Dissertation, Grenoble Alpes. Accessed April 24, 2019. http://www.theses.fr/2016GREAM028.

MLA Handbook (7th Edition):

Do, Cao Tri. “Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification.” 2016. Web. 24 Apr 2019.

Vancouver:

Do CT. Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2016. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2016GREAM028.

Council of Science Editors:

Do CT. Apprentissage de métrique temporelle multi-modale et multi-échelle pour la classification robuste de séries temporelles par plus proches voisins : Multi-modal and multi-scale temporal metric learning for robust nearest neighbors classification. [Doctoral Dissertation]. Grenoble Alpes; 2016. Available from: http://www.theses.fr/2016GREAM028


Florida International University

21. Zhou, Wubai. Data Mining Techniques to Understand Textual Data.

Degree: PhD, Computer Science, 2017, Florida International University

  More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents,… (more)

Subjects/Keywords: Data Mining; Textual Understanding; Domain Adaptation; Text Summarization; Deep Learning; Learn to Rank; Metric Learning; Computer Sciences; Physical Sciences and Mathematics

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APA (6th Edition):

Zhou, W. (2017). Data Mining Techniques to Understand Textual Data. (Doctoral Dissertation). Florida International University. Retrieved from http://digitalcommons.fiu.edu/etd/3493 ; FIDC003998

Chicago Manual of Style (16th Edition):

Zhou, Wubai. “Data Mining Techniques to Understand Textual Data.” 2017. Doctoral Dissertation, Florida International University. Accessed April 24, 2019. http://digitalcommons.fiu.edu/etd/3493 ; FIDC003998.

MLA Handbook (7th Edition):

Zhou, Wubai. “Data Mining Techniques to Understand Textual Data.” 2017. Web. 24 Apr 2019.

Vancouver:

Zhou W. Data Mining Techniques to Understand Textual Data. [Internet] [Doctoral dissertation]. Florida International University; 2017. [cited 2019 Apr 24]. Available from: http://digitalcommons.fiu.edu/etd/3493 ; FIDC003998.

Council of Science Editors:

Zhou W. Data Mining Techniques to Understand Textual Data. [Doctoral Dissertation]. Florida International University; 2017. Available from: http://digitalcommons.fiu.edu/etd/3493 ; FIDC003998

22. Bucher, Maxime. Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval.

Degree: Docteur es, Informatique, 2018, Normandie

Dans cette thèse nous étudions différentes questions relatives à la mise en pratique de modèles d'apprentissage profond. En effet malgré les avancées prometteuses de ces… (more)

Subjects/Keywords: Classification zero-shot; Attribapprentissage de métriqueuts; Goulot d'étranglement sémantique; Zero-shot learning; Attributes; Embedding,; Metric learning; Semantic bottleneck; Retrieval

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

APA (6th Edition):

Bucher, M. (2018). Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval. (Doctoral Dissertation). Normandie. Retrieved from http://www.theses.fr/2018NORMC250

Chicago Manual of Style (16th Edition):

Bucher, Maxime. “Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval.” 2018. Doctoral Dissertation, Normandie. Accessed April 24, 2019. http://www.theses.fr/2018NORMC250.

MLA Handbook (7th Edition):

Bucher, Maxime. “Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval.” 2018. Web. 24 Apr 2019.

Vancouver:

Bucher M. Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval. [Internet] [Doctoral dissertation]. Normandie; 2018. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2018NORMC250.

Council of Science Editors:

Bucher M. Apprentissage et exploitation de représentations sémantiques pour la classification et la recherche d'images : Learning and exploiting semantic representations for image classification and retrieval. [Doctoral Dissertation]. Normandie; 2018. Available from: http://www.theses.fr/2018NORMC250

23. Perrot, Michaël. Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé.

Degree: Docteur es, Informatique, 2016, Lyon

 De nombreux algorithmes en Apprentissage Automatique utilisent une notion de distance ou de similarité entre les exemples pour résoudre divers problèmes tels que la classification,… (more)

Subjects/Keywords: Intelligence artificielle; Apprentissage automatique; Apprentissage statistique; Apprentissage des métriques; Théorie de l'apprentissage; Artificial intelligence; Machine learning; Statistical learning; Metric learning; Learning theory

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APA (6th Edition):

Perrot, M. (2016). Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSES072

Chicago Manual of Style (16th Edition):

Perrot, Michaël. “Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé.” 2016. Doctoral Dissertation, Lyon. Accessed April 24, 2019. http://www.theses.fr/2016LYSES072.

MLA Handbook (7th Edition):

Perrot, Michaël. “Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé.” 2016. Web. 24 Apr 2019.

Vancouver:

Perrot M. Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2016LYSES072.

Council of Science Editors:

Perrot M. Theory and algorithms for learning metrics with controlled behaviour : Théorie et algorithmes pour l'apprentissage de métriques à comportement contrôlé. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSES072


Texas A&M University

24. Li, Shuo. Matrix Analysis of Communication and Brain Networks.

Degree: 2016, Texas A&M University

 In this dissertation, we study two network problems using matrices as our primary analysis tools. First, the limits of treating interference as noise are studied… (more)

Subjects/Keywords: Interference management; Treating interference as noise; Deterministic channel; Resting-state fMRI time-series; Unsupervised clustering; Metric learning; Mahalanobis distance; Matrix analysis

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APA (6th Edition):

Li, S. (2016). Matrix Analysis of Communication and Brain Networks. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/159008

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, Shuo. “Matrix Analysis of Communication and Brain Networks.” 2016. Thesis, Texas A&M University. Accessed April 24, 2019. http://hdl.handle.net/1969.1/159008.

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

MLA Handbook (7th Edition):

Li, Shuo. “Matrix Analysis of Communication and Brain Networks.” 2016. Web. 24 Apr 2019.

Vancouver:

Li S. Matrix Analysis of Communication and Brain Networks. [Internet] [Thesis]. Texas A&M University; 2016. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1969.1/159008.

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

Council of Science Editors:

Li S. Matrix Analysis of Communication and Brain Networks. [Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/159008

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

25. Law, Marc Teva. Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web.

Degree: Docteur es, Informatique, 2015, Université Pierre et Marie Curie – Paris VI

Cette thèse se focalise sur l'apprentissage de distance pour la comparaison d'images ou de pages Web. Les distances (ou métriques) sont exploitées dans divers contextes… (more)

Subjects/Keywords: Apprentissage de métrique; Reconnaissance d'image; Vision par ordinateur; Régularisation; Fantope; Distance; Metric learning; Images recognition; 006.6

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APA (6th Edition):

Law, M. T. (2015). Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web. (Doctoral Dissertation). Université Pierre et Marie Curie – Paris VI. Retrieved from http://www.theses.fr/2015PA066019

Chicago Manual of Style (16th Edition):

Law, Marc Teva. “Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web.” 2015. Doctoral Dissertation, Université Pierre et Marie Curie – Paris VI. Accessed April 24, 2019. http://www.theses.fr/2015PA066019.

MLA Handbook (7th Edition):

Law, Marc Teva. “Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web.” 2015. Web. 24 Apr 2019.

Vancouver:

Law MT. Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web. [Internet] [Doctoral dissertation]. Université Pierre et Marie Curie – Paris VI; 2015. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2015PA066019.

Council of Science Editors:

Law MT. Distance metric learning for image and webpage comparison : Apprentissage de distance pour la comparaison d'images et de pages Web. [Doctoral Dissertation]. Université Pierre et Marie Curie – Paris VI; 2015. Available from: http://www.theses.fr/2015PA066019


Linnaeus University

26. Kasianenko, Stanislav. Predicting Software Defectiveness by Mining Software Repositories.

Degree: computer science and media technology (CM), 2018, Linnaeus University

  One of the important aims of the continuous software development process is to localize and remove all existing program bugs as fast as possible.… (more)

Subjects/Keywords: repository mining; software metric; correlation; defect; bug; natural language processing; Pearson coefficient; Breiman’s decision tree; machine learning; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Kasianenko, S. (2018). Predicting Software Defectiveness by Mining Software Repositories. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729

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):

Kasianenko, Stanislav. “Predicting Software Defectiveness by Mining Software Repositories.” 2018. Thesis, Linnaeus University. Accessed April 24, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.

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

MLA Handbook (7th Edition):

Kasianenko, Stanislav. “Predicting Software Defectiveness by Mining Software Repositories.” 2018. Web. 24 Apr 2019.

Vancouver:

Kasianenko S. Predicting Software Defectiveness by Mining Software Repositories. [Internet] [Thesis]. Linnaeus University; 2018. [cited 2019 Apr 24]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.

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

Council of Science Editors:

Kasianenko S. Predicting Software Defectiveness by Mining Software Repositories. [Thesis]. Linnaeus University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729

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

27. Saxena, Shreyas. Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition.

Degree: Docteur es, Informatique, 2016, Grenoble Alpes

 Dans cette dissertation, nous proposons des méthodes d’apprentissage automa-tique aptes à bénéficier de la récente explosion des volumes de données digitales.Premièrement nous considérons l’amélioration de… (more)

Subjects/Keywords: Apprentissage de métriques locales; Transfert d’apprentissage; Réseaux neuronaux convolutionnels; Apprentissage d’architectures; Local metric learning; Transfer learning; Convolutional neural network; Architecture learning; 004

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

APA (6th Edition):

Saxena, S. (2016). Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition. (Doctoral Dissertation). Grenoble Alpes. Retrieved from http://www.theses.fr/2016GREAM080

Chicago Manual of Style (16th Edition):

Saxena, Shreyas. “Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition.” 2016. Doctoral Dissertation, Grenoble Alpes. Accessed April 24, 2019. http://www.theses.fr/2016GREAM080.

MLA Handbook (7th Edition):

Saxena, Shreyas. “Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition.” 2016. Web. 24 Apr 2019.

Vancouver:

Saxena S. Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition. [Internet] [Doctoral dissertation]. Grenoble Alpes; 2016. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2016GREAM080.

Council of Science Editors:

Saxena S. Apprentissage de représentations pour la reconnaissance visuelle : Learning representations for visual recognition. [Doctoral Dissertation]. Grenoble Alpes; 2016. Available from: http://www.theses.fr/2016GREAM080

28. Bellet, Aurélien. Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation.

Degree: Docteur es, Informatique, 2012, Saint-Etienne

 Ces dernières années, l'importance cruciale des métriques en apprentissage automatique a mené à un intérêt grandissant pour l'optimisation de distances et de similarités en utilisant… (more)

Subjects/Keywords: Apprentissage de métriques; Apprentissage statistique; Optimisation convexe; Classification; Données structurées; Distance d'édition; Bornes en généralisation; Metric learning; Statistical learning; Convex optimization; Classification; Structured data; Edit distance; Generalization bounds

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APA (6th Edition):

Bellet, A. (2012). Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation. (Doctoral Dissertation). Saint-Etienne. Retrieved from http://www.theses.fr/2012STET4003

Chicago Manual of Style (16th Edition):

Bellet, Aurélien. “Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation.” 2012. Doctoral Dissertation, Saint-Etienne. Accessed April 24, 2019. http://www.theses.fr/2012STET4003.

MLA Handbook (7th Edition):

Bellet, Aurélien. “Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation.” 2012. Web. 24 Apr 2019.

Vancouver:

Bellet A. Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation. [Internet] [Doctoral dissertation]. Saint-Etienne; 2012. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2012STET4003.

Council of Science Editors:

Bellet A. Supervised metric learning with generalization guarantees : Apprentissage supervisé de métriques avec garanties en généralisation. [Doctoral Dissertation]. Saint-Etienne; 2012. Available from: http://www.theses.fr/2012STET4003

29. Lajugie, Rémi. Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data.

Degree: Docteur es, Informatique, 2015, Paris, Ecole normale supérieure

Dans cette thèse nous nous intéressons à des problèmes d’apprentissage automatique dans le cadre de sorties structurées avec une structure séquentielle. D’une part, nous considérons… (more)

Subjects/Keywords: Apprentissage; Faible supervision; Prédiction structurée; Apprentissage de métrique; Alignement musique sur partition; Déformation temporelle; Machine learning; Weak supervision; Structured prediction; Metric learning; Music to partition alignment; Time warping; 004

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

APA (6th Edition):

Lajugie, R. (2015). Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data. (Doctoral Dissertation). Paris, Ecole normale supérieure. Retrieved from http://www.theses.fr/2015ENSU0024

Chicago Manual of Style (16th Edition):

Lajugie, Rémi. “Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data.” 2015. Doctoral Dissertation, Paris, Ecole normale supérieure. Accessed April 24, 2019. http://www.theses.fr/2015ENSU0024.

MLA Handbook (7th Edition):

Lajugie, Rémi. “Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data.” 2015. Web. 24 Apr 2019.

Vancouver:

Lajugie R. Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data. [Internet] [Doctoral dissertation]. Paris, Ecole normale supérieure; 2015. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2015ENSU0024.

Council of Science Editors:

Lajugie R. Prédiction structurée pour l’analyse de données séquentielles : Structured prediction for sequential data. [Doctoral Dissertation]. Paris, Ecole normale supérieure; 2015. Available from: http://www.theses.fr/2015ENSU0024

30. Zheng, Lilei. Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois.

Degree: Docteur es, Informatique, 2016, Lyon

Dans de nombreux problèmes d’apprentissage automatique et de reconnaissance des formes, il y a toujours un besoin de fonctions métriques appropriées pour mesurer la distance… (more)

Subjects/Keywords: Informatique; Reconnaissance de formes; Fonction métrique; Apprentissage du métrique; Vérification de paires; Réduction de dimension; Visualisation de données; Similarité triangulaire; Information Technology; Pattern recognition; Metric function; Metric learning; Pariwise verification; Dimensionality reduction; Data visualization; Triangulair similarity; 006.407 2

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

APA (6th Edition):

Zheng, L. (2016). Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSEI045

Chicago Manual of Style (16th Edition):

Zheng, Lilei. “Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois.” 2016. Doctoral Dissertation, Lyon. Accessed April 24, 2019. http://www.theses.fr/2016LYSEI045.

MLA Handbook (7th Edition):

Zheng, Lilei. “Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois.” 2016. Web. 24 Apr 2019.

Vancouver:

Zheng L. Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2019 Apr 24]. Available from: http://www.theses.fr/2016LYSEI045.

Council of Science Editors:

Zheng L. Triangular similarity metric learning : A siamese architecture approach : Apprentissage métrique de similarité triangulaire : Une approche d'architecture siamois. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSEI045

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