Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(approximate nearest neighbor search). Showing records 1 – 30 of 4921 total matches.

[1] [2] [3] [4] [5] … [165]

Search Limiters

Last 2 Years | English Only

Degrees

Levels

Languages

Country

▼ Search Limiters


University of Lethbridge

1. University of Lethbridge. Faculty of Arts and Science. A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing .

Degree: 2015, University of Lethbridge

 This thesis proposes a k-nearest-neighbor search method inspired by the grid space partitioning and the compact trie tree structure. A detailed implementation based on the… (more)

Subjects/Keywords: nearest neighbor search; trie

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Science, U. o. L. F. o. A. a. (2015). A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing . (Thesis). University of Lethbridge. Retrieved from http://hdl.handle.net/10133/3865

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):

Science, University of Lethbridge. Faculty of Arts and. “A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing .” 2015. Thesis, University of Lethbridge. Accessed December 09, 2019. http://hdl.handle.net/10133/3865.

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

MLA Handbook (7th Edition):

Science, University of Lethbridge. Faculty of Arts and. “A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing .” 2015. Web. 09 Dec 2019.

Vancouver:

Science UoLFoAa. A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing . [Internet] [Thesis]. University of Lethbridge; 2015. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10133/3865.

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

Council of Science Editors:

Science UoLFoAa. A nearest neighbor search method suitable for low dimensions and location-dependent spatial queries in mobile computing . [Thesis]. University of Lethbridge; 2015. Available from: http://hdl.handle.net/10133/3865

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


Anna University

2. Balasaravanan K. Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;.

Degree: Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions, 2015, Anna University

Spatial database comprises of database that is optimized to store and newlinequery the data that is in a way related to objects involved in space… (more)

Subjects/Keywords: Bayesian Nearest Neighbor Search; Nearest Neighbor; Optimal bayesian

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

K, B. (2015). Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/40239

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):

K, Balasaravanan. “Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;.” 2015. Thesis, Anna University. Accessed December 09, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/40239.

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

MLA Handbook (7th Edition):

K, Balasaravanan. “Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;.” 2015. Web. 09 Dec 2019.

Vancouver:

K B. Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;. [Internet] [Thesis]. Anna University; 2015. [cited 2019 Dec 09]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40239.

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

Council of Science Editors:

K B. Parallel spatial object search with Optimal bayesian nearest neighbor in Grid regions;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40239

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


EPFL

3. Trzciński, Tomasz Piotr. Learning and Matching Binary Local Feature Descriptors.

Degree: 2014, EPFL

Subjects/Keywords: computer vision; machine learning; binary local feature descriptors; binary approximate nearest neighbor search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Trzciński, T. P. (2014). Learning and Matching Binary Local Feature Descriptors. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/200862

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):

Trzciński, Tomasz Piotr. “Learning and Matching Binary Local Feature Descriptors.” 2014. Thesis, EPFL. Accessed December 09, 2019. http://infoscience.epfl.ch/record/200862.

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

MLA Handbook (7th Edition):

Trzciński, Tomasz Piotr. “Learning and Matching Binary Local Feature Descriptors.” 2014. Web. 09 Dec 2019.

Vancouver:

Trzciński TP. Learning and Matching Binary Local Feature Descriptors. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Dec 09]. Available from: http://infoscience.epfl.ch/record/200862.

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

Council of Science Editors:

Trzciński TP. Learning and Matching Binary Local Feature Descriptors. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/200862

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


McMaster University

4. Lu, Yangdi. Salient Index for Similarity Search Over High Dimensional Vectors.

Degree: MSc, 2018, McMaster University

The approximate nearest neighbor(ANN) search over high dimensional data has become an unavoidable service for online applications. Fast and high-quality results of unknown queries are… (more)

Subjects/Keywords: approximate nearest neighbor; locality sensitive hashing; index structure; searching strategy

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lu, Y. (2018). Salient Index for Similarity Search Over High Dimensional Vectors. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23970

Chicago Manual of Style (16th Edition):

Lu, Yangdi. “Salient Index for Similarity Search Over High Dimensional Vectors.” 2018. Masters Thesis, McMaster University. Accessed December 09, 2019. http://hdl.handle.net/11375/23970.

MLA Handbook (7th Edition):

Lu, Yangdi. “Salient Index for Similarity Search Over High Dimensional Vectors.” 2018. Web. 09 Dec 2019.

Vancouver:

Lu Y. Salient Index for Similarity Search Over High Dimensional Vectors. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/11375/23970.

Council of Science Editors:

Lu Y. Salient Index for Similarity Search Over High Dimensional Vectors. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23970

5. Bekele, Esubalew Tamirat. Object detection and localization using approximate nearest neighbor search: random tree implementation.

Degree: MS, Electrical Engineering, 2009, Vanderbilt University

  A machineâs intelligence is related to its autonomy. Making robots autonomous is perhaps the most difficult current challenge in the state-of-the art of robotics… (more)

Subjects/Keywords: approximate nearest neighbor search; nearest neighbor search; object recognition; random search tree; object localization; ann; object detection

…pure nearest neighbor search method… …38 Equation 7: Algorithm of pure nearest neighbor search… …1 ANN: Approximate Nearest Neighbor… …Chapter 4 is devoted to the description of the approximate nearest neighbor algorithm used in… …trees are used to find the nearest neighbor. Chapter 5 is the presentation of the approximate… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Bekele, E. T. (2009). Object detection and localization using approximate nearest neighbor search: random tree implementation. (Masters Thesis). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu//available/etd-03302009-162240/ ;

Chicago Manual of Style (16th Edition):

Bekele, Esubalew Tamirat. “Object detection and localization using approximate nearest neighbor search: random tree implementation.” 2009. Masters Thesis, Vanderbilt University. Accessed December 09, 2019. http://etd.library.vanderbilt.edu//available/etd-03302009-162240/ ;.

MLA Handbook (7th Edition):

Bekele, Esubalew Tamirat. “Object detection and localization using approximate nearest neighbor search: random tree implementation.” 2009. Web. 09 Dec 2019.

Vancouver:

Bekele ET. Object detection and localization using approximate nearest neighbor search: random tree implementation. [Internet] [Masters thesis]. Vanderbilt University; 2009. [cited 2019 Dec 09]. Available from: http://etd.library.vanderbilt.edu//available/etd-03302009-162240/ ;.

Council of Science Editors:

Bekele ET. Object detection and localization using approximate nearest neighbor search: random tree implementation. [Masters Thesis]. Vanderbilt University; 2009. Available from: http://etd.library.vanderbilt.edu//available/etd-03302009-162240/ ;


University of Alberta

6. Hajebi, Kiana. Efficient Visual Search in Appearance-based SLAM.

Degree: PhD, Department of Computing Science, 2015, University of Alberta

 Simultaneous localization and mapping (SLAM) in an unknown environment is a prerequisite to have a truly autonomous mobile robot. In this thesis, we focus on… (more)

Subjects/Keywords: Graph Nearest Neighbor Search; Image Retrieval; Robotics; Appearance-based SLAM

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hajebi, K. (2015). Efficient Visual Search in Appearance-based SLAM. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/k643b385g

Chicago Manual of Style (16th Edition):

Hajebi, Kiana. “Efficient Visual Search in Appearance-based SLAM.” 2015. Doctoral Dissertation, University of Alberta. Accessed December 09, 2019. https://era.library.ualberta.ca/files/k643b385g.

MLA Handbook (7th Edition):

Hajebi, Kiana. “Efficient Visual Search in Appearance-based SLAM.” 2015. Web. 09 Dec 2019.

Vancouver:

Hajebi K. Efficient Visual Search in Appearance-based SLAM. [Internet] [Doctoral dissertation]. University of Alberta; 2015. [cited 2019 Dec 09]. Available from: https://era.library.ualberta.ca/files/k643b385g.

Council of Science Editors:

Hajebi K. Efficient Visual Search in Appearance-based SLAM. [Doctoral Dissertation]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/k643b385g


University of California – San Diego

7. Zhai, Zhen. Fast tree based nearest neighbor search.

Degree: Computer Science, 2017, University of California – San Diego

Nearest neighbor search is a basic primitive method used for machine learning and information retrieval. We look at exact nearest neighbor search algorithms using tree… (more)

Subjects/Keywords: Computer science; Algorithm; Machine Learning; Nearest Neighbor Search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zhai, Z. (2017). Fast tree based nearest neighbor search. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/514265kf

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):

Zhai, Zhen. “Fast tree based nearest neighbor search.” 2017. Thesis, University of California – San Diego. Accessed December 09, 2019. http://www.escholarship.org/uc/item/514265kf.

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

MLA Handbook (7th Edition):

Zhai, Zhen. “Fast tree based nearest neighbor search.” 2017. Web. 09 Dec 2019.

Vancouver:

Zhai Z. Fast tree based nearest neighbor search. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2019 Dec 09]. Available from: http://www.escholarship.org/uc/item/514265kf.

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

Council of Science Editors:

Zhai Z. Fast tree based nearest neighbor search. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/514265kf

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


University of Illinois – Urbana-Champaign

8. Kumar, Nirman. In search of better proximity.

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

 Given a set of points in a metric space, a fundamental problem is to preprocess these points for answering nearest-neighbor queries on them. Proximity search(more)

Subjects/Keywords: Computational Geometry; Algorithms; Data-Structures; Nearest-Neighbor Search; Approximation algorithms

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kumar, N. (2014). In search of better proximity. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50535

Chicago Manual of Style (16th Edition):

Kumar, Nirman. “In search of better proximity.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 09, 2019. http://hdl.handle.net/2142/50535.

MLA Handbook (7th Edition):

Kumar, Nirman. “In search of better proximity.” 2014. Web. 09 Dec 2019.

Vancouver:

Kumar N. In search of better proximity. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2142/50535.

Council of Science Editors:

Kumar N. In search of better proximity. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50535


Colorado State University

9. Zhang, Hao. Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets.

Degree: PhD, Computer Science, 2016, Colorado State University

Nearest neighbor search is an important operation whose goal is to find items in the dataset that are similar to a given query. It has… (more)

Subjects/Keywords: Hashing; Nearest neighbor search; Unsupervised; Large-scale dataset; Binary code; Quantization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zhang, H. (2016). Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/173342

Chicago Manual of Style (16th Edition):

Zhang, Hao. “Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets.” 2016. Doctoral Dissertation, Colorado State University. Accessed December 09, 2019. http://hdl.handle.net/10217/173342.

MLA Handbook (7th Edition):

Zhang, Hao. “Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets.” 2016. Web. 09 Dec 2019.

Vancouver:

Zhang H. Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets. [Internet] [Doctoral dissertation]. Colorado State University; 2016. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10217/173342.

Council of Science Editors:

Zhang H. Unsupervised binary code learning for approximate nearest neighbor search in large-scale datasets. [Doctoral Dissertation]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/173342


Rochester Institute of Technology

10. Allmann, Josh. Approximate nearest neighbors for recognition of foreground and background in images and video.

Degree: Computer Science (GCCIS), 2013, Rochester Institute of Technology

 Problems in image matching, saliency detection in images, and background detection in video are studied. Algorithms based on approximate nearest-neighbor matching are proposed to solve… (more)

Subjects/Keywords: Approximate nearest neighbor; Background detection; Foreground detection; Kd-tree; Saliency; Walsh-hadamard transform

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Allmann, J. (2013). Approximate nearest neighbors for recognition of foreground and background in images and video. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/5513

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):

Allmann, Josh. “Approximate nearest neighbors for recognition of foreground and background in images and video.” 2013. Thesis, Rochester Institute of Technology. Accessed December 09, 2019. https://scholarworks.rit.edu/theses/5513.

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

MLA Handbook (7th Edition):

Allmann, Josh. “Approximate nearest neighbors for recognition of foreground and background in images and video.” 2013. Web. 09 Dec 2019.

Vancouver:

Allmann J. Approximate nearest neighbors for recognition of foreground and background in images and video. [Internet] [Thesis]. Rochester Institute of Technology; 2013. [cited 2019 Dec 09]. Available from: https://scholarworks.rit.edu/theses/5513.

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

Council of Science Editors:

Allmann J. Approximate nearest neighbors for recognition of foreground and background in images and video. [Thesis]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/5513

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


Boston University

11. Cakir, Fatih. Online hashing for fast similarity search.

Degree: PhD, Computer Science, 2017, Boston University

 In this thesis, the problem of online adaptive hashing for fast similarity search is studied. Similarity search is a central problem in many computer vision… (more)

Subjects/Keywords: Computer science; Hashing; Nearest neighbor search; Online hashing; Online learning; Retrieval; Similarity search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Cakir, F. (2017). Online hashing for fast similarity search. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/27360

Chicago Manual of Style (16th Edition):

Cakir, Fatih. “Online hashing for fast similarity search.” 2017. Doctoral Dissertation, Boston University. Accessed December 09, 2019. http://hdl.handle.net/2144/27360.

MLA Handbook (7th Edition):

Cakir, Fatih. “Online hashing for fast similarity search.” 2017. Web. 09 Dec 2019.

Vancouver:

Cakir F. Online hashing for fast similarity search. [Internet] [Doctoral dissertation]. Boston University; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2144/27360.

Council of Science Editors:

Cakir F. Online hashing for fast similarity search. [Doctoral Dissertation]. Boston University; 2017. Available from: http://hdl.handle.net/2144/27360


Kyoto University

12. Tagami, Yukihiro. Practical Web-scale Recommender Systems .

Degree: 2018, Kyoto University

Subjects/Keywords: Recommender systems; Online advertising; Extreme multi-label classification; Learning-to-rank; Approximate nearest neighbor search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Tagami, Y. (2018). Practical Web-scale Recommender Systems . (Thesis). Kyoto University. Retrieved from http://hdl.handle.net/2433/235110

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):

Tagami, Yukihiro. “Practical Web-scale Recommender Systems .” 2018. Thesis, Kyoto University. Accessed December 09, 2019. http://hdl.handle.net/2433/235110.

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

MLA Handbook (7th Edition):

Tagami, Yukihiro. “Practical Web-scale Recommender Systems .” 2018. Web. 09 Dec 2019.

Vancouver:

Tagami Y. Practical Web-scale Recommender Systems . [Internet] [Thesis]. Kyoto University; 2018. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2433/235110.

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

Council of Science Editors:

Tagami Y. Practical Web-scale Recommender Systems . [Thesis]. Kyoto University; 2018. Available from: http://hdl.handle.net/2433/235110

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

13. Chafik, Sanaa. Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information.

Degree: Docteur es, Informatique, 2017, Paris Saclay; Université Hassan II (Casablanca, Maroc)

 Avec l’évolution des technologies numériques et la prolifération d'internet, la quantité d’information numérique a considérablement évolué. La recherche par similarité (ou recherche des plus proches… (more)

Subjects/Keywords: Indexation multidimensionnelle; Apprentissage non supervisé; Hachage; Recherche des plus proches voisins; Recherche par le contenu de l'information; Apprentissage profond; Multidimensionnal indexing; Unsupervised learning; Hashing; Approximate nearest neighbor search; Content based information retrieval (CBMR); Deep learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chafik, S. (2017). Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information. (Doctoral Dissertation). Paris Saclay; Université Hassan II (Casablanca, Maroc). Retrieved from http://www.theses.fr/2017SACLL008

Chicago Manual of Style (16th Edition):

Chafik, Sanaa. “Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information.” 2017. Doctoral Dissertation, Paris Saclay; Université Hassan II (Casablanca, Maroc). Accessed December 09, 2019. http://www.theses.fr/2017SACLL008.

MLA Handbook (7th Edition):

Chafik, Sanaa. “Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information.” 2017. Web. 09 Dec 2019.

Vancouver:

Chafik S. Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information. [Internet] [Doctoral dissertation]. Paris Saclay; Université Hassan II (Casablanca, Maroc); 2017. [cited 2019 Dec 09]. Available from: http://www.theses.fr/2017SACLL008.

Council of Science Editors:

Chafik S. Machine learning techniques for content-based information retrieval : Méthodes d’apprentissage automatique pour la recherche par le contenu de l’information. [Doctoral Dissertation]. Paris Saclay; Université Hassan II (Casablanca, Maroc); 2017. Available from: http://www.theses.fr/2017SACLL008

14. Venâncio, José Carlos Ferreira. Localização de Precisão Usando Redes Sem Fios Enterradas.

Degree: 2015, Instituto Politécnico do Porto

As Redes Sem Fios Enterradas (Wireless Underground Networks - WUN) são formadas por nós que comunicam entre si através de ligações sem fios e têm… (more)

Subjects/Keywords: Wireless Underground Networks (WUN); Posicionamento; Wi-Fi; Search Nearest Neighbor; Positioning; Telecomunicações

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Venâncio, J. C. F. (2015). Localização de Precisão Usando Redes Sem Fios Enterradas. (Thesis). Instituto Politécnico do Porto. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8115

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):

Venâncio, José Carlos Ferreira. “Localização de Precisão Usando Redes Sem Fios Enterradas.” 2015. Thesis, Instituto Politécnico do Porto. Accessed December 09, 2019. https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8115.

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

MLA Handbook (7th Edition):

Venâncio, José Carlos Ferreira. “Localização de Precisão Usando Redes Sem Fios Enterradas.” 2015. Web. 09 Dec 2019.

Vancouver:

Venâncio JCF. Localização de Precisão Usando Redes Sem Fios Enterradas. [Internet] [Thesis]. Instituto Politécnico do Porto; 2015. [cited 2019 Dec 09]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8115.

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

Council of Science Editors:

Venâncio JCF. Localização de Precisão Usando Redes Sem Fios Enterradas. [Thesis]. Instituto Politécnico do Porto; 2015. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8115

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


University of Alberta

15. Shahbazi, Hossein. Application of locality sensitive hashing to feature matching and loop closure detection.

Degree: MS, Department of Computing Science, 2011, University of Alberta

 My thesis focuses on automatic parameter selection for euclidean distance version of Locality Sensitive Hashing (LSH) and solving visual loop closure detection by using LSH.… (more)

Subjects/Keywords: locality sensitive hashing; nearest neighbor search; parameter selection; visual slam; loop closure detection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shahbazi, H. (2011). Application of locality sensitive hashing to feature matching and loop closure detection. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cc08hg50x

Chicago Manual of Style (16th Edition):

Shahbazi, Hossein. “Application of locality sensitive hashing to feature matching and loop closure detection.” 2011. Masters Thesis, University of Alberta. Accessed December 09, 2019. https://era.library.ualberta.ca/files/cc08hg50x.

MLA Handbook (7th Edition):

Shahbazi, Hossein. “Application of locality sensitive hashing to feature matching and loop closure detection.” 2011. Web. 09 Dec 2019.

Vancouver:

Shahbazi H. Application of locality sensitive hashing to feature matching and loop closure detection. [Internet] [Masters thesis]. University of Alberta; 2011. [cited 2019 Dec 09]. Available from: https://era.library.ualberta.ca/files/cc08hg50x.

Council of Science Editors:

Shahbazi H. Application of locality sensitive hashing to feature matching and loop closure detection. [Masters Thesis]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/cc08hg50x

16. José Silva Leite, Pedro. Massively parallel nearest neighbors searches in dynamic point clouds on GPU .

Degree: 2010, Universidade Federal de Pernambuco

 Esta dissertação introduz uma estrutura de dados baseada em gride implementada em GPU. Ela foi desenvolvida para pesquisa dos vizinhos mais próximos em nuvens de… (more)

Subjects/Keywords: PBR; Point-based rendering; ANN; KNN; GPGPU; Massive parallel programming; Nearest neighbor search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

José Silva Leite, P. (2010). Massively parallel nearest neighbors searches in dynamic point clouds on GPU . (Thesis). Universidade Federal de Pernambuco. Retrieved from http://repositorio.ufpe.br/handle/123456789/2356

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):

José Silva Leite, Pedro. “Massively parallel nearest neighbors searches in dynamic point clouds on GPU .” 2010. Thesis, Universidade Federal de Pernambuco. Accessed December 09, 2019. http://repositorio.ufpe.br/handle/123456789/2356.

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

MLA Handbook (7th Edition):

José Silva Leite, Pedro. “Massively parallel nearest neighbors searches in dynamic point clouds on GPU .” 2010. Web. 09 Dec 2019.

Vancouver:

José Silva Leite P. Massively parallel nearest neighbors searches in dynamic point clouds on GPU . [Internet] [Thesis]. Universidade Federal de Pernambuco; 2010. [cited 2019 Dec 09]. Available from: http://repositorio.ufpe.br/handle/123456789/2356.

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

Council of Science Editors:

José Silva Leite P. Massively parallel nearest neighbors searches in dynamic point clouds on GPU . [Thesis]. Universidade Federal de Pernambuco; 2010. Available from: http://repositorio.ufpe.br/handle/123456789/2356

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


George Mason University

17. Grossman, Stanley I. An Automated Directed Spectral Search Methodology for Small Target Detection .

Degree: 2014, George Mason University

 Much of the current efforts in remote sensing tackle macro-level problems such as determining the extent of wheat in a field, the general health of… (more)

Subjects/Keywords: Remote sensing; analytic sweet spot; automated target detection; directed search; nearest neighbor inflation; spectral searach

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Grossman, S. I. (2014). An Automated Directed Spectral Search Methodology for Small Target Detection . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8885

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):

Grossman, Stanley I. “An Automated Directed Spectral Search Methodology for Small Target Detection .” 2014. Thesis, George Mason University. Accessed December 09, 2019. http://hdl.handle.net/1920/8885.

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

MLA Handbook (7th Edition):

Grossman, Stanley I. “An Automated Directed Spectral Search Methodology for Small Target Detection .” 2014. Web. 09 Dec 2019.

Vancouver:

Grossman SI. An Automated Directed Spectral Search Methodology for Small Target Detection . [Internet] [Thesis]. George Mason University; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1920/8885.

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

Council of Science Editors:

Grossman SI. An Automated Directed Spectral Search Methodology for Small Target Detection . [Thesis]. George Mason University; 2014. Available from: http://hdl.handle.net/1920/8885

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


New Jersey Institute of Technology

18. Ma, Xiguo. Design and analysis of algorithms for similarity search based on intrinsic dimension.

Degree: PhD, Computer Science, 2014, New Jersey Institute of Technology

  One of the most fundamental operations employed in data mining tasks such as classification, cluster analysis, and anomaly detection, is that of similarity search.… (more)

Subjects/Keywords: Similarity research; Nearest neighbor; Intrinsic dimension; Dimensional testing; Multi-step search; Aggregation; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ma, X. (2014). Design and analysis of algorithms for similarity search based on intrinsic dimension. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/102

Chicago Manual of Style (16th Edition):

Ma, Xiguo. “Design and analysis of algorithms for similarity search based on intrinsic dimension.” 2014. Doctoral Dissertation, New Jersey Institute of Technology. Accessed December 09, 2019. https://digitalcommons.njit.edu/dissertations/102.

MLA Handbook (7th Edition):

Ma, Xiguo. “Design and analysis of algorithms for similarity search based on intrinsic dimension.” 2014. Web. 09 Dec 2019.

Vancouver:

Ma X. Design and analysis of algorithms for similarity search based on intrinsic dimension. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2014. [cited 2019 Dec 09]. Available from: https://digitalcommons.njit.edu/dissertations/102.

Council of Science Editors:

Ma X. Design and analysis of algorithms for similarity search based on intrinsic dimension. [Doctoral Dissertation]. New Jersey Institute of Technology; 2014. Available from: https://digitalcommons.njit.edu/dissertations/102


University of Newcastle

19. Walker, Josiah. Improved similarity search for large data in machine learning and robotics.

Degree: PhD, 2017, University of Newcastle

Research Doctorate - Doctor of Philosophy (PhD)

This thesis presents techniques for accelerating similarity search methods on large datasets. Similarity search has applications in clustering,… (more)

Subjects/Keywords: nearest neighbour search; cover trees; approximate search; locality sensitive hashing; boosting; learning to search; similarity and distance learning; big data; large scale learning; reverse nearest neighbour search

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Walker, J. (2017). Improved similarity search for large data in machine learning and robotics. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1333823

Chicago Manual of Style (16th Edition):

Walker, Josiah. “Improved similarity search for large data in machine learning and robotics.” 2017. Doctoral Dissertation, University of Newcastle. Accessed December 09, 2019. http://hdl.handle.net/1959.13/1333823.

MLA Handbook (7th Edition):

Walker, Josiah. “Improved similarity search for large data in machine learning and robotics.” 2017. Web. 09 Dec 2019.

Vancouver:

Walker J. Improved similarity search for large data in machine learning and robotics. [Internet] [Doctoral dissertation]. University of Newcastle; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1959.13/1333823.

Council of Science Editors:

Walker J. Improved similarity search for large data in machine learning and robotics. [Doctoral Dissertation]. University of Newcastle; 2017. Available from: http://hdl.handle.net/1959.13/1333823


Colorado State University

20. Yu, Zhixian. One-shot learning with pretrained convolutional neural network.

Degree: MS(M.S.), Computer Science, 2019, Colorado State University

 Recent progress in convolutional neural networks and deep learning has revolutionized the image classification field, and computers can now classify images with a very high… (more)

Subjects/Keywords: convolutional neural network; image recognition; proximity forest; generalized curvature analysis; approximate nearest neighbor; one-shot learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yu, Z. (2019). One-shot learning with pretrained convolutional neural network. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/197309

Chicago Manual of Style (16th Edition):

Yu, Zhixian. “One-shot learning with pretrained convolutional neural network.” 2019. Masters Thesis, Colorado State University. Accessed December 09, 2019. http://hdl.handle.net/10217/197309.

MLA Handbook (7th Edition):

Yu, Zhixian. “One-shot learning with pretrained convolutional neural network.” 2019. Web. 09 Dec 2019.

Vancouver:

Yu Z. One-shot learning with pretrained convolutional neural network. [Internet] [Masters thesis]. Colorado State University; 2019. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10217/197309.

Council of Science Editors:

Yu Z. One-shot learning with pretrained convolutional neural network. [Masters Thesis]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/197309


University of Southern California

21. Cheong, Hye-Yeon. Error tolerance approach for similarity search problems.

Degree: PhD, Electrical Engineering (Multimedia & Creative Technology), 2011, University of Southern California

 As the system complexity increases and VLSI chip circuit becomes more highly condensed and integrated towards nano-scale, the requirement of 100% exact execution of designed… (more)

Subjects/Keywords: nearest neighbor search; similarity search; approximation algorithm; error tolerance; QNNM; motion estimation; vector quantization; multiple faults modeling

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Cheong, H. (2011). Error tolerance approach for similarity search problems. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/428824/rec/2417

Chicago Manual of Style (16th Edition):

Cheong, Hye-Yeon. “Error tolerance approach for similarity search problems.” 2011. Doctoral Dissertation, University of Southern California. Accessed December 09, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/428824/rec/2417.

MLA Handbook (7th Edition):

Cheong, Hye-Yeon. “Error tolerance approach for similarity search problems.” 2011. Web. 09 Dec 2019.

Vancouver:

Cheong H. Error tolerance approach for similarity search problems. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2019 Dec 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/428824/rec/2417.

Council of Science Editors:

Cheong H. Error tolerance approach for similarity search problems. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/428824/rec/2417


University of Cincinnati

22. CALENDER, CHRISTOPHER R. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.

Degree: MS, Engineering : Computer Science, 2004, University of Cincinnati

 There are often times where an application needs to find the N nearest neighbors. Imagine the scene of an accident and someone needs to know… (more)

Subjects/Keywords: Computer Science; nearest neighbor; cluster; K-means; clustering; approximate nearest neighbor; approximate k-means

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

CALENDER, C. R. (2004). APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952

Chicago Manual of Style (16th Edition):

CALENDER, CHRISTOPHER R. “APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.” 2004. Masters Thesis, University of Cincinnati. Accessed December 09, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

MLA Handbook (7th Edition):

CALENDER, CHRISTOPHER R. “APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT.” 2004. Web. 09 Dec 2019.

Vancouver:

CALENDER CR. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. [Internet] [Masters thesis]. University of Cincinnati; 2004. [cited 2019 Dec 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952.

Council of Science Editors:

CALENDER CR. APPROXIMATE N-NEAREST NEIGHBOR CLUSTERING ON DISTRIBUTED DATABASES USING ITERATIVE REFINEMENT. [Masters Thesis]. University of Cincinnati; 2004. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092929952

23. Pham, The Anh. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.

Degree: Docteur es, Informatique, 2013, Université François-Rabelais de Tours

Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières… (more)

Subjects/Keywords: Détection de jonctions; Caractérisation de jonctions; Détection de points d’intérêt; Documents graphiques; Images de trait; Recherche approximative de plus proches voisins; Indexation de caractéristiques; Arbres de clustering; Junction detection; Junction characterization; Junction distortion; Topology correction; Edge grouping; Dominant point detection; Graphical documents; Line- drawings; Approximate nearest neighbor search; Feature indexing; Locality-sensitive hashing; Clustering trees

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Pham, T. A. (2013). Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. (Doctoral Dissertation). Université François-Rabelais de Tours. Retrieved from http://www.theses.fr/2013TOUR4023

Chicago Manual of Style (16th Edition):

Pham, The Anh. “Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.” 2013. Doctoral Dissertation, Université François-Rabelais de Tours. Accessed December 09, 2019. http://www.theses.fr/2013TOUR4023.

MLA Handbook (7th Edition):

Pham, The Anh. “Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space.” 2013. Web. 09 Dec 2019.

Vancouver:

Pham TA. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. [Internet] [Doctoral dissertation]. Université François-Rabelais de Tours; 2013. [cited 2019 Dec 09]. Available from: http://www.theses.fr/2013TOUR4023.

Council of Science Editors:

Pham TA. Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension : Robust junction for line-drawing images and time-efficient feature indexing in feature vector space. [Doctoral Dissertation]. Université François-Rabelais de Tours; 2013. Available from: http://www.theses.fr/2013TOUR4023


University of Cincinnati

24. Gupta, Nidhi. Mutual k Nearest Neighbor based Classifier.

Degree: MS, Engineering and Applied Science: Computer Science, 2010, University of Cincinnati

 In this information intensive world, data is used to make statistical decisions for business, scientific, and industrial situations. With decreasing cost of computing power and… (more)

Subjects/Keywords: Artificial Intelligence; K Nearest Neighbor; Mutual k Nearest Neighbor; Classifier; Classification algorithm; Reverse Nearest Neighbor

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Gupta, N. (2010). Mutual k Nearest Neighbor based Classifier. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369

Chicago Manual of Style (16th Edition):

Gupta, Nidhi. “Mutual k Nearest Neighbor based Classifier.” 2010. Masters Thesis, University of Cincinnati. Accessed December 09, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

MLA Handbook (7th Edition):

Gupta, Nidhi. “Mutual k Nearest Neighbor based Classifier.” 2010. Web. 09 Dec 2019.

Vancouver:

Gupta N. Mutual k Nearest Neighbor based Classifier. [Internet] [Masters thesis]. University of Cincinnati; 2010. [cited 2019 Dec 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369.

Council of Science Editors:

Gupta N. Mutual k Nearest Neighbor based Classifier. [Masters Thesis]. University of Cincinnati; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1289937369

25. Kalantidis, Yannis. Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων.

Degree: 2014, National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ)

 New applications that exploit the huge data volume in community photo collections are emerging every day and visual image search is therefore becoming increasingly important.… (more)

Subjects/Keywords: Ομαδοποίηση; Αναζήτηση κοντινότερου γείτονα; Αναζήτηση εικόνων; Clustering; Nearest neighbor search; Computer vision; Machine learning; Image retrieval

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kalantidis, Y. (2014). Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων. (Thesis). National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ). Retrieved from http://hdl.handle.net/10442/hedi/38870

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):

Kalantidis, Yannis. “Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων.” 2014. Thesis, National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ). Accessed December 09, 2019. http://hdl.handle.net/10442/hedi/38870.

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

MLA Handbook (7th Edition):

Kalantidis, Yannis. “Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων.” 2014. Web. 09 Dec 2019.

Vancouver:

Kalantidis Y. Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων. [Internet] [Thesis]. National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ); 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10442/hedi/38870.

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

Council of Science Editors:

Kalantidis Y. Τεχνικές ομαδοποίησης και κοντινότερου γείτονα για οπτική αναζήτηση εικόνων. [Thesis]. National Technical University of Athens (NTUA); Εθνικό Μετσόβιο Πολυτεχνείο (ΕΜΠ); 2014. Available from: http://hdl.handle.net/10442/hedi/38870

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


University of Southern California

26. Demiryurek, Ugur. Query processing in time-dependent spatial networks.

Degree: PhD, Computer Science, 2014, University of Southern California

 Recent advances in online map services and their wide deployment in hand-held devices and car-navigation systems have led to extensive use of location-based services. The… (more)

Subjects/Keywords: k nearest neighbor search; road networks; shortest path; spatial networks; time-dependent road networks; time-dependent shortest path

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Demiryurek, U. (2014). Query processing in time-dependent spatial networks. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/120837/rec/5369

Chicago Manual of Style (16th Edition):

Demiryurek, Ugur. “Query processing in time-dependent spatial networks.” 2014. Doctoral Dissertation, University of Southern California. Accessed December 09, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/120837/rec/5369.

MLA Handbook (7th Edition):

Demiryurek, Ugur. “Query processing in time-dependent spatial networks.” 2014. Web. 09 Dec 2019.

Vancouver:

Demiryurek U. Query processing in time-dependent spatial networks. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Dec 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/120837/rec/5369.

Council of Science Editors:

Demiryurek U. Query processing in time-dependent spatial networks. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/120837/rec/5369


Colorado State University

27. O'Hara, Stephen. Scalable learning of actions from unlabeled videos.

Degree: PhD, Computer Science, 2007, Colorado State University

 Emerging applications in human-computer interfaces, security, and robotics have a need for understanding human behavior from video data. Much of the research in the field… (more)

Subjects/Keywords: action recognition; approximate nearest neighbor; Grassmann manifold; randomized forests; unsupervised learning; video analysis

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

O'Hara, S. (2007). Scalable learning of actions from unlabeled videos. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/78864

Chicago Manual of Style (16th Edition):

O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2007. Doctoral Dissertation, Colorado State University. Accessed December 09, 2019. http://hdl.handle.net/10217/78864.

MLA Handbook (7th Edition):

O'Hara, Stephen. “Scalable learning of actions from unlabeled videos.” 2007. Web. 09 Dec 2019.

Vancouver:

O'Hara S. Scalable learning of actions from unlabeled videos. [Internet] [Doctoral dissertation]. Colorado State University; 2007. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10217/78864.

Council of Science Editors:

O'Hara S. Scalable learning of actions from unlabeled videos. [Doctoral Dissertation]. Colorado State University; 2007. Available from: http://hdl.handle.net/10217/78864

28. Jain, Himalaya. Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle.

Degree: Docteur es, Informatique, 2018, Rennes 1

Cette thèse aborde le problème de la recherche d'images à grande échelle. Pour aborder la recherche d'images à grande échelle, il est nécessaire de coder… (more)

Subjects/Keywords: Indexation; Recherche des plus proches voisins; Recherche d'images à grande échelle; Apprentissage supervisé de représentation; Compression; Indexing; Nearest neighbor search; Large scale Image search; Supervised Representation learning; Compression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Jain, H. (2018). Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle. (Doctoral Dissertation). Rennes 1. Retrieved from http://www.theses.fr/2018REN1S027

Chicago Manual of Style (16th Edition):

Jain, Himalaya. “Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle.” 2018. Doctoral Dissertation, Rennes 1. Accessed December 09, 2019. http://www.theses.fr/2018REN1S027.

MLA Handbook (7th Edition):

Jain, Himalaya. “Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle.” 2018. Web. 09 Dec 2019.

Vancouver:

Jain H. Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle. [Internet] [Doctoral dissertation]. Rennes 1; 2018. [cited 2019 Dec 09]. Available from: http://www.theses.fr/2018REN1S027.

Council of Science Editors:

Jain H. Learning compact representations for large scale image search : Apprentissage de représentations compactes pour la recherche d'images à grande échelle. [Doctoral Dissertation]. Rennes 1; 2018. Available from: http://www.theses.fr/2018REN1S027

29. Ram, Parikshit. New paradigms for approximate nearest-neighbor search.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new… (more)

Subjects/Keywords: Similarity search; Nearest-neighbor search; Computational geometry; Algorithms and analysis; Nearest neighbor analysis (Statistics); Approximation algorithms; Search theory

…paradigm for approximate nearest-neighbor search – consider the nearest-neighbor error (… …exact and approximate nearest-neighbor search inapplicable to the general problem of maxkernel… …for an approximate nearest-neighbor of the query. . . . . . . . . . . . . . . . 8 3 4… …bar represents the speedup of treebased exact nearest-neighbor search, the blue bars… …larger and larger regions in search for the nearest-neighbor. 89 31 Defeatist-forest search… 

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ram, P. (2013). New paradigms for approximate nearest-neighbor search. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49112

Chicago Manual of Style (16th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Doctoral Dissertation, Georgia Tech. Accessed December 09, 2019. http://hdl.handle.net/1853/49112.

MLA Handbook (7th Edition):

Ram, Parikshit. “New paradigms for approximate nearest-neighbor search.” 2013. Web. 09 Dec 2019.

Vancouver:

Ram P. New paradigms for approximate nearest-neighbor search. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1853/49112.

Council of Science Editors:

Ram P. New paradigms for approximate nearest-neighbor search. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49112

30. Jorge Aikes Junior. Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP.

Degree: 2012, Universidade Estadual do Oeste do Parana

SÃries temporais podem ser entendidas como qualquer conjunto de observaÃÃes que se encontram ordenadas no tempo. Dentre as vÃrias tarefas possÃveis com dados temporais, uma… (more)

Subjects/Keywords: SISTEMAS DINAMICOS; sÃries temporais; previsÃo; k-Nearest Neighbor - Time Series Prediction; time series; forecasting; k-Nearest Neighbor - Time Series Prediction, k-Nearest Neighbor; k-Nearest Neighbor

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Junior, J. A. (2012). Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP. (Thesis). Universidade Estadual do Oeste do Parana. Retrieved from http://tede.unioeste.br/tede//tde_busca/arquivo.php?codArquivo=909

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):

Junior, Jorge Aikes. “Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP.” 2012. Thesis, Universidade Estadual do Oeste do Parana. Accessed December 09, 2019. http://tede.unioeste.br/tede//tde_busca/arquivo.php?codArquivo=909.

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

MLA Handbook (7th Edition):

Junior, Jorge Aikes. “Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP.” 2012. Web. 09 Dec 2019.

Vancouver:

Junior JA. Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP. [Internet] [Thesis]. Universidade Estadual do Oeste do Parana; 2012. [cited 2019 Dec 09]. Available from: http://tede.unioeste.br/tede//tde_busca/arquivo.php?codArquivo=909.

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

Council of Science Editors:

Junior JA. Estudo da influÃncia de diversas medidas de similaridade na previsÃo de sÃries temporais utilizando o algoritmo KNN-TSP. [Thesis]. Universidade Estadual do Oeste do Parana; 2012. Available from: http://tede.unioeste.br/tede//tde_busca/arquivo.php?codArquivo=909

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

[1] [2] [3] [4] [5] … [165]

.