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:(Spectral Learning). Showing records 1 – 30 of 57 total matches.

[1] [2]

Search Limiters

Last 2 Years | English Only

▼ Search Limiters


University of Oxford

1. Samo, Yves-Laurent Kom. Advances in kernel methods : towards general-purpose and scalable models.

Degree: PhD, 2017, University of Oxford

 A wide range of statistical and machine learning problems involve learning one or multiple latent functions, or properties thereof, from datasets. Examples include regression, classification,… (more)

Subjects/Keywords: 006.3; Machine learning; Generalized Spectral Kernels

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Samo, Y. K. (2017). Advances in kernel methods : towards general-purpose and scalable models. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:e0ff5f8c-bc28-4d96-8ddb-2d49152b2eee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729809

Chicago Manual of Style (16th Edition):

Samo, Yves-Laurent Kom. “Advances in kernel methods : towards general-purpose and scalable models.” 2017. Doctoral Dissertation, University of Oxford. Accessed October 16, 2019. http://ora.ox.ac.uk/objects/uuid:e0ff5f8c-bc28-4d96-8ddb-2d49152b2eee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729809.

MLA Handbook (7th Edition):

Samo, Yves-Laurent Kom. “Advances in kernel methods : towards general-purpose and scalable models.” 2017. Web. 16 Oct 2019.

Vancouver:

Samo YK. Advances in kernel methods : towards general-purpose and scalable models. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2019 Oct 16]. Available from: http://ora.ox.ac.uk/objects/uuid:e0ff5f8c-bc28-4d96-8ddb-2d49152b2eee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729809.

Council of Science Editors:

Samo YK. Advances in kernel methods : towards general-purpose and scalable models. [Doctoral Dissertation]. University of Oxford; 2017. Available from: http://ora.ox.ac.uk/objects/uuid:e0ff5f8c-bc28-4d96-8ddb-2d49152b2eee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.729809


Wright State University

2. Puladas, Charan. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.

Degree: MSEE, Electrical Engineering, 2016, Wright State University

  The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop… (more)

Subjects/Keywords: Electrical Engineering; Remote Sensing; Computer Science; Hyperspectral Imaging; Spectral Unmixing; Spectral Estimation; Endmember Variablity; Spectral Correlation; Signal Processing; Machine Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Puladas, C. (2016). Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. (Masters Thesis). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356

Chicago Manual of Style (16th Edition):

Puladas, Charan. “Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.” 2016. Masters Thesis, Wright State University. Accessed October 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356.

MLA Handbook (7th Edition):

Puladas, Charan. “Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.” 2016. Web. 16 Oct 2019.

Vancouver:

Puladas C. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. [Internet] [Masters thesis]. Wright State University; 2016. [cited 2019 Oct 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356.

Council of Science Editors:

Puladas C. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. [Masters Thesis]. Wright State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356


Brigham Young University

3. Drake, Adam C. Practical Improvements in Applied Spectral Learning.

Degree: PhD, 2010, Brigham Young University

Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to… (more)

Subjects/Keywords: spectral learning; Fourier-based learning; Fourier transform; machine learning; search algorithms; sentiment analysis; Computer Sciences

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Drake, A. C. (2010). Practical Improvements in Applied Spectral Learning. (Doctoral Dissertation). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3545&context=etd

Chicago Manual of Style (16th Edition):

Drake, Adam C. “Practical Improvements in Applied Spectral Learning.” 2010. Doctoral Dissertation, Brigham Young University. Accessed October 16, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3545&context=etd.

MLA Handbook (7th Edition):

Drake, Adam C. “Practical Improvements in Applied Spectral Learning.” 2010. Web. 16 Oct 2019.

Vancouver:

Drake AC. Practical Improvements in Applied Spectral Learning. [Internet] [Doctoral dissertation]. Brigham Young University; 2010. [cited 2019 Oct 16]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3545&context=etd.

Council of Science Editors:

Drake AC. Practical Improvements in Applied Spectral Learning. [Doctoral Dissertation]. Brigham Young University; 2010. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3545&context=etd


University of Sydney

4. Uezato, Tatsumi. Unmixing of hyperspectral data by incorporating spectral variability and spatial information .

Degree: 2016, University of Sydney

Spectral unmixing enables quantitative information on the abundances of cover types to be estimated within each image pixel. Although many spectral unmixing methods have been… (more)

Subjects/Keywords: Hyperspectral imagery; spectral unmixing; endmember; image processing; machine learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Uezato, T. (2016). Unmixing of hyperspectral data by incorporating spectral variability and spatial information . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15929

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):

Uezato, Tatsumi. “Unmixing of hyperspectral data by incorporating spectral variability and spatial information .” 2016. Thesis, University of Sydney. Accessed October 16, 2019. http://hdl.handle.net/2123/15929.

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

MLA Handbook (7th Edition):

Uezato, Tatsumi. “Unmixing of hyperspectral data by incorporating spectral variability and spatial information .” 2016. Web. 16 Oct 2019.

Vancouver:

Uezato T. Unmixing of hyperspectral data by incorporating spectral variability and spatial information . [Internet] [Thesis]. University of Sydney; 2016. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/2123/15929.

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

Council of Science Editors:

Uezato T. Unmixing of hyperspectral data by incorporating spectral variability and spatial information . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/15929

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


University of Tennessee – Knoxville

5. Qu, Ying. Hyperspectral Image Analysis through Unsupervised Deep Learning.

Degree: 2018, University of Tennessee – Knoxville

 Hyperspectral image (HSI) analysis has become an active research area in computer vision field with a wide range of applications. However, in order to yield… (more)

Subjects/Keywords: hyperspectral image; spectral unmixing; pansharpening; anomaly detection; autoencoder; unsupervised deep learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Qu, Y. (2018). Hyperspectral Image Analysis through Unsupervised Deep Learning. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/4870

Chicago Manual of Style (16th Edition):

Qu, Ying. “Hyperspectral Image Analysis through Unsupervised Deep Learning.” 2018. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed October 16, 2019. https://trace.tennessee.edu/utk_graddiss/4870.

MLA Handbook (7th Edition):

Qu, Ying. “Hyperspectral Image Analysis through Unsupervised Deep Learning.” 2018. Web. 16 Oct 2019.

Vancouver:

Qu Y. Hyperspectral Image Analysis through Unsupervised Deep Learning. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2018. [cited 2019 Oct 16]. Available from: https://trace.tennessee.edu/utk_graddiss/4870.

Council of Science Editors:

Qu Y. Hyperspectral Image Analysis through Unsupervised Deep Learning. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2018. Available from: https://trace.tennessee.edu/utk_graddiss/4870


Carnegie Mellon University

6. Boots, Byron. Spectral Approaches to Learning Predictive Representations.

Degree: 2012, Carnegie Mellon University

 A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must obtain… (more)

Subjects/Keywords: System Identification; Reinforcement Learning; Spectral Learning; Predictive State Representations; Kernel Methods; Artificial Intelligence and Robotics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Boots, B. (2012). Spectral Approaches to Learning Predictive Representations. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/131

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):

Boots, Byron. “Spectral Approaches to Learning Predictive Representations.” 2012. Thesis, Carnegie Mellon University. Accessed October 16, 2019. http://repository.cmu.edu/dissertations/131.

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

MLA Handbook (7th Edition):

Boots, Byron. “Spectral Approaches to Learning Predictive Representations.” 2012. Web. 16 Oct 2019.

Vancouver:

Boots B. Spectral Approaches to Learning Predictive Representations. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2019 Oct 16]. Available from: http://repository.cmu.edu/dissertations/131.

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

Council of Science Editors:

Boots B. Spectral Approaches to Learning Predictive Representations. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/131

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


University of Georgia

7. Shi, Yang. Brain controlled robot navigation based on low cost EEG.

Degree: MS, Computer Science, 2017, University of Georgia

 This thesis focuses on employing low cost EEG signals to control robots for navigation tasks. A data driven signal processing and machine learning framework is… (more)

Subjects/Keywords: EEG; BCI; Brain wave; Machine learning; Quadratic Discriminant Analysis; Principle Component Analysis; Spectral Analysis; Power Spectral Density

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shi, Y. (2017). Brain controlled robot navigation based on low cost EEG. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37928

Chicago Manual of Style (16th Edition):

Shi, Yang. “Brain controlled robot navigation based on low cost EEG.” 2017. Masters Thesis, University of Georgia. Accessed October 16, 2019. http://hdl.handle.net/10724/37928.

MLA Handbook (7th Edition):

Shi, Yang. “Brain controlled robot navigation based on low cost EEG.” 2017. Web. 16 Oct 2019.

Vancouver:

Shi Y. Brain controlled robot navigation based on low cost EEG. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10724/37928.

Council of Science Editors:

Shi Y. Brain controlled robot navigation based on low cost EEG. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37928


Cornell University

8. Topel, Spencer. Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval .

Degree: 2012, Cornell University

 Music exemplifies the repetitive patterns in nature. These patterns lend a distinctiveness to sound sources that make them identifiable. In audio analysis, this information can… (more)

Subjects/Keywords: music; spectralism; music information; machine learning; audio analysis; cage; grisey; spectral decomposition

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Topel, S. (2012). Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/31493

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):

Topel, Spencer. “Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval .” 2012. Thesis, Cornell University. Accessed October 16, 2019. http://hdl.handle.net/1813/31493.

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

MLA Handbook (7th Edition):

Topel, Spencer. “Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval .” 2012. Web. 16 Oct 2019.

Vancouver:

Topel S. Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval . [Internet] [Thesis]. Cornell University; 2012. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/1813/31493.

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

Council of Science Editors:

Topel S. Extensible Spectralism: Revealing Latent Structures In Music Audio For Composition, Analysis, And Retrieval . [Thesis]. Cornell University; 2012. Available from: http://hdl.handle.net/1813/31493

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


University of California – Irvine

9. Huang, Furong. Discovery of Latent Factors in High-dimensional Data Using Tensor Methods.

Degree: Electrical and Computer Engineering, 2016, University of California – Irvine

 Unsupervised learning aims at the discovery of hidden structure that drives the observations in the real world. It is essential for success in modern machine… (more)

Subjects/Keywords: Computer science; Latent Variable Graphical Model; Spectral Methods; Tensor Decomposition; Unsupervised Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huang, F. (2016). Discovery of Latent Factors in High-dimensional Data Using Tensor Methods. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/97f3404j

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):

Huang, Furong. “Discovery of Latent Factors in High-dimensional Data Using Tensor Methods.” 2016. Thesis, University of California – Irvine. Accessed October 16, 2019. http://www.escholarship.org/uc/item/97f3404j.

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

MLA Handbook (7th Edition):

Huang, Furong. “Discovery of Latent Factors in High-dimensional Data Using Tensor Methods.” 2016. Web. 16 Oct 2019.

Vancouver:

Huang F. Discovery of Latent Factors in High-dimensional Data Using Tensor Methods. [Internet] [Thesis]. University of California – Irvine; 2016. [cited 2019 Oct 16]. Available from: http://www.escholarship.org/uc/item/97f3404j.

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

Council of Science Editors:

Huang F. Discovery of Latent Factors in High-dimensional Data Using Tensor Methods. [Thesis]. University of California – Irvine; 2016. Available from: http://www.escholarship.org/uc/item/97f3404j

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


Duke University

10. Wang, Lu. Statistical Modeling of Brain Network Data .

Degree: 2018, Duke University

  There has been an increasing interest in using brain imaging technologies to better understand the relationship between brain structural connectivity networks  – also known… (more)

Subjects/Keywords: Statistics; Brain connectomics; Multiple graphs; Network regression; Spectral embedding; Subgraph learning; Symmetric bilinear regression

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wang, L. (2018). Statistical Modeling of Brain Network Data . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16875

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):

Wang, Lu. “Statistical Modeling of Brain Network Data .” 2018. Thesis, Duke University. Accessed October 16, 2019. http://hdl.handle.net/10161/16875.

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

MLA Handbook (7th Edition):

Wang, Lu. “Statistical Modeling of Brain Network Data .” 2018. Web. 16 Oct 2019.

Vancouver:

Wang L. Statistical Modeling of Brain Network Data . [Internet] [Thesis]. Duke University; 2018. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10161/16875.

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

Council of Science Editors:

Wang L. Statistical Modeling of Brain Network Data . [Thesis]. Duke University; 2018. Available from: http://hdl.handle.net/10161/16875

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


University of Ontario Institute of Technology

11. Gultepe, Eren. Pushing the limits of traditional unsupervised learning.

Degree: 2018, University of Ontario Institute of Technology

 Unsupervised learning has important applications in extremely large data settings such as in medical, biological, social, and environmental data. Typically in these settings, copious amounts… (more)

Subjects/Keywords: Unsupervised feature learning; Cluster significance testing; Latent semantic analysis; Spectral clustering; Independent component analysis

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Gultepe, E. (2018). Pushing the limits of traditional unsupervised learning. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/951

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):

Gultepe, Eren. “Pushing the limits of traditional unsupervised learning.” 2018. Thesis, University of Ontario Institute of Technology. Accessed October 16, 2019. http://hdl.handle.net/10155/951.

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

MLA Handbook (7th Edition):

Gultepe, Eren. “Pushing the limits of traditional unsupervised learning.” 2018. Web. 16 Oct 2019.

Vancouver:

Gultepe E. Pushing the limits of traditional unsupervised learning. [Internet] [Thesis]. University of Ontario Institute of Technology; 2018. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10155/951.

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

Council of Science Editors:

Gultepe E. Pushing the limits of traditional unsupervised learning. [Thesis]. University of Ontario Institute of Technology; 2018. Available from: http://hdl.handle.net/10155/951

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


University of Minnesota

12. Ubaru, Shashanka. Algorithmic advances in learning from large dimensional matrices and scientific data.

Degree: PhD, Computer Science, 2018, University of Minnesota

 This thesis is devoted to answering a range of questions in machine learning and data analysis related to large dimensional matrices and scientific data. Two… (more)

Subjects/Keywords: Data Analysis; Error Correcting codes; Machine learning; Matrix approximation; Matrix spectral sums; Numerical linear Algebra

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ubaru, S. (2018). Algorithmic advances in learning from large dimensional matrices and scientific data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/199004

Chicago Manual of Style (16th Edition):

Ubaru, Shashanka. “Algorithmic advances in learning from large dimensional matrices and scientific data.” 2018. Doctoral Dissertation, University of Minnesota. Accessed October 16, 2019. http://hdl.handle.net/11299/199004.

MLA Handbook (7th Edition):

Ubaru, Shashanka. “Algorithmic advances in learning from large dimensional matrices and scientific data.” 2018. Web. 16 Oct 2019.

Vancouver:

Ubaru S. Algorithmic advances in learning from large dimensional matrices and scientific data. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/11299/199004.

Council of Science Editors:

Ubaru S. Algorithmic advances in learning from large dimensional matrices and scientific data. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/199004

13. Jamal, Sara. Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data.

Degree: Docteur es, Sciences de la terre et de l'univers, espace. Astrophysique et cosmologie, 2017, Aix Marseille Université

Les futurs sondages à grande échelle, comme la mission Euclid, produiront un large set de données qui nécessitera la mise en place de chaînes de… (more)

Subjects/Keywords: Analyse spectrale; Mesure de redshift; Machine Learning; Euclid; Traitement de données; Spectral analysis; Redshift measurement; Machine Learning; Euclid; Data processing

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Jamal, S. (2017). Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data. (Doctoral Dissertation). Aix Marseille Université. Retrieved from http://www.theses.fr/2017AIXM0263

Chicago Manual of Style (16th Edition):

Jamal, Sara. “Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data.” 2017. Doctoral Dissertation, Aix Marseille Université. Accessed October 16, 2019. http://www.theses.fr/2017AIXM0263.

MLA Handbook (7th Edition):

Jamal, Sara. “Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data.” 2017. Web. 16 Oct 2019.

Vancouver:

Jamal S. Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data. [Internet] [Doctoral dissertation]. Aix Marseille Université 2017. [cited 2019 Oct 16]. Available from: http://www.theses.fr/2017AIXM0263.

Council of Science Editors:

Jamal S. Analyse spectrale des données du sondage Euclid : Spectral analysis of the Euclid survey data. [Doctoral Dissertation]. Aix Marseille Université 2017. Available from: http://www.theses.fr/2017AIXM0263


KTH

14. Javanmardi, Ramtin. Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

Alzheimer’s is one of the most costly illnesses that exists today and the number of people with alzheimers diease is expected to increase with… (more)

Subjects/Keywords: EEG; Alzheimer's; Machine learning; Supervised machine learning; Spectral power; Feature extraction; Support vector machine; svm; knn; lda; Computer Sciences; Datavetenskap (datalogi)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Javanmardi, R. (2018). Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229650

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):

Javanmardi, Ramtin. “Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning.” 2018. Thesis, KTH. Accessed October 16, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229650.

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

MLA Handbook (7th Edition):

Javanmardi, Ramtin. “Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning.” 2018. Web. 16 Oct 2019.

Vancouver:

Javanmardi R. Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning. [Internet] [Thesis]. KTH; 2018. [cited 2019 Oct 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229650.

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

Council of Science Editors:

Javanmardi R. Classification of Healthy and Alzheimer's Patients Using Electroencephalography and Supervised Machine Learning. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229650

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


University of Pennsylvania

15. Dhillon, Paramveer. Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging.

Degree: 2014, University of Pennsylvania

Spectral learning algorithms are becoming increasingly popular in data-rich domains, driven in part by recent advances in large scale randomized SVD, and in spectral estimation… (more)

Subjects/Keywords: Brain/Medical Imaging; Dimensionality Reduction; Machine Learning; Natural Language Processing; Spectral Learning; Statistics; Computer Sciences; Radiology; Statistics and Probability

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Dhillon, P. (2014). Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/1257

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):

Dhillon, Paramveer. “Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging.” 2014. Thesis, University of Pennsylvania. Accessed October 16, 2019. https://repository.upenn.edu/edissertations/1257.

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

MLA Handbook (7th Edition):

Dhillon, Paramveer. “Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging.” 2014. Web. 16 Oct 2019.

Vancouver:

Dhillon P. Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging. [Internet] [Thesis]. University of Pennsylvania; 2014. [cited 2019 Oct 16]. Available from: https://repository.upenn.edu/edissertations/1257.

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

Council of Science Editors:

Dhillon P. Advances in Spectral Learning with Applications to Text Analysis and Brain Imaging. [Thesis]. University of Pennsylvania; 2014. Available from: https://repository.upenn.edu/edissertations/1257

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


University of Wisconsin – Milwaukee

16. Bashiri, Fereshteh S. Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach.

Degree: PhD, Engineering, 2019, University of Wisconsin – Milwaukee

  In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications… (more)

Subjects/Keywords: Deep learning; Descriptive model; Image Processing; Manifold learning; Spectral analysis; Artificial Intelligence and Robotics; Computer Sciences; Electrical and Electronics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Bashiri, F. S. (2019). Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach. (Doctoral Dissertation). University of Wisconsin – Milwaukee. Retrieved from https://dc.uwm.edu/etd/2043

Chicago Manual of Style (16th Edition):

Bashiri, Fereshteh S. “Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach.” 2019. Doctoral Dissertation, University of Wisconsin – Milwaukee. Accessed October 16, 2019. https://dc.uwm.edu/etd/2043.

MLA Handbook (7th Edition):

Bashiri, Fereshteh S. “Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach.” 2019. Web. 16 Oct 2019.

Vancouver:

Bashiri FS. Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach. [Internet] [Doctoral dissertation]. University of Wisconsin – Milwaukee; 2019. [cited 2019 Oct 16]. Available from: https://dc.uwm.edu/etd/2043.

Council of Science Editors:

Bashiri FS. Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach. [Doctoral Dissertation]. University of Wisconsin – Milwaukee; 2019. Available from: https://dc.uwm.edu/etd/2043


Arizona State University

17. Sun, Liang. Multi-Label Dimensionality Reduction.

Degree: PhD, Computer Science, 2011, Arizona State University

 Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning,… (more)

Subjects/Keywords: Computer Science; canonical correlation analysis; dimensionality reduction; hypergraph spectral learning; multi-label learning; partial least squares

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Sun, L. (2011). Multi-Label Dimensionality Reduction. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/9454

Chicago Manual of Style (16th Edition):

Sun, Liang. “Multi-Label Dimensionality Reduction.” 2011. Doctoral Dissertation, Arizona State University. Accessed October 16, 2019. http://repository.asu.edu/items/9454.

MLA Handbook (7th Edition):

Sun, Liang. “Multi-Label Dimensionality Reduction.” 2011. Web. 16 Oct 2019.

Vancouver:

Sun L. Multi-Label Dimensionality Reduction. [Internet] [Doctoral dissertation]. Arizona State University; 2011. [cited 2019 Oct 16]. Available from: http://repository.asu.edu/items/9454.

Council of Science Editors:

Sun L. Multi-Label Dimensionality Reduction. [Doctoral Dissertation]. Arizona State University; 2011. Available from: http://repository.asu.edu/items/9454


University of Windsor

18. Li, Yifeng. Sparse machine learning models in bioinformatics.

Degree: PhD, Computer Science, 2014, University of Windsor

  The meaning of parsimony is twofold in machine learning: either the structure or (and) the parameter of a model can be sparse. Sparse models… (more)

Subjects/Keywords: Biological sciences; Applied sciences; High-order dynamic bayesian network; Linearmodels; Sparse machine learning models; Sparse representation; Spectral clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Li, Y. (2014). Sparse machine learning models in bioinformatics. (Doctoral Dissertation). University of Windsor. Retrieved from http://scholar.uwindsor.ca/etd/5023

Chicago Manual of Style (16th Edition):

Li, Yifeng. “Sparse machine learning models in bioinformatics.” 2014. Doctoral Dissertation, University of Windsor. Accessed October 16, 2019. http://scholar.uwindsor.ca/etd/5023.

MLA Handbook (7th Edition):

Li, Yifeng. “Sparse machine learning models in bioinformatics.” 2014. Web. 16 Oct 2019.

Vancouver:

Li Y. Sparse machine learning models in bioinformatics. [Internet] [Doctoral dissertation]. University of Windsor; 2014. [cited 2019 Oct 16]. Available from: http://scholar.uwindsor.ca/etd/5023.

Council of Science Editors:

Li Y. Sparse machine learning models in bioinformatics. [Doctoral Dissertation]. University of Windsor; 2014. Available from: http://scholar.uwindsor.ca/etd/5023


Carnegie Mellon University

19. Huang, Tzu-Kuo. Exploiting Non-Sequence Data in Dynamic Model Learning.

Degree: 2013, Carnegie Mellon University

 Virtually all methods of learning dynamic models from data start from the same basic assumption: that the learning algorithm will be provided with a single… (more)

Subjects/Keywords: Dynamic Model; Vector Auto-regression; Hidden Markov Mode l; Latent Vari- able Model; Expectation Maximization; Spectral Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huang, T. (2013). Exploiting Non-Sequence Data in Dynamic Model Learning. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/561

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):

Huang, Tzu-Kuo. “Exploiting Non-Sequence Data in Dynamic Model Learning.” 2013. Thesis, Carnegie Mellon University. Accessed October 16, 2019. http://repository.cmu.edu/dissertations/561.

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

MLA Handbook (7th Edition):

Huang, Tzu-Kuo. “Exploiting Non-Sequence Data in Dynamic Model Learning.” 2013. Web. 16 Oct 2019.

Vancouver:

Huang T. Exploiting Non-Sequence Data in Dynamic Model Learning. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2019 Oct 16]. Available from: http://repository.cmu.edu/dissertations/561.

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

Council of Science Editors:

Huang T. Exploiting Non-Sequence Data in Dynamic Model Learning. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/561

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


Virginia Tech

20. Liu, Chenang. Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control.

Degree: PhD, Industrial and Systems Engineering, 2019, Virginia Tech

 Additive manufacturing (AM) is a powerful emerging technology for fabrication of components with complex geometries using a variety of materials. However, despite promising potential, due… (more)

Subjects/Keywords: Additive Manufacturing; Online Quality Assurance; Data Analytics; Spectral Graph Theory; Manifold Learning; Bilateral Time Series Model; Closed-Loop Control

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Liu, C. (2019). Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91900

Chicago Manual of Style (16th Edition):

Liu, Chenang. “Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control.” 2019. Doctoral Dissertation, Virginia Tech. Accessed October 16, 2019. http://hdl.handle.net/10919/91900.

MLA Handbook (7th Edition):

Liu, Chenang. “Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control.” 2019. Web. 16 Oct 2019.

Vancouver:

Liu C. Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10919/91900.

Council of Science Editors:

Liu C. Smart Additive Manufacturing Using Advanced Data Analytics and Closed Loop Control. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91900

21. Deeb, Rada. Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale.

Degree: Docteur es, Electronique, microelectronique, optique et lasers, optoelectronique microondes robotique, 2018, Lyon

 Dans cette thèse, nous étudions un phénomène optique souvent ignoré en vision par ordinateur : les inter-réflexions. Les inter-réflexions, qui peuvent être trouvées dans l’état… (more)

Subjects/Keywords: Inter-réflexions; Estimation de réflectance spectrale; Constance de couleur; Apprentissage profond; Interreflections; Spectral reflectance estimate; Constancy of color; Deep learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Deeb, R. (2018). Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2018LYSES035

Chicago Manual of Style (16th Edition):

Deeb, Rada. “Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale.” 2018. Doctoral Dissertation, Lyon. Accessed October 16, 2019. http://www.theses.fr/2018LYSES035.

MLA Handbook (7th Edition):

Deeb, Rada. “Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale.” 2018. Web. 16 Oct 2019.

Vancouver:

Deeb R. Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale. [Internet] [Doctoral dissertation]. Lyon; 2018. [cited 2019 Oct 16]. Available from: http://www.theses.fr/2018LYSES035.

Council of Science Editors:

Deeb R. Inter-reflections in computer vision : importance, modeling & application in spectral estimation : Inter-réflexion en vision par ordinateur : importance, modélisation and application en estimation spectrale. [Doctoral Dissertation]. Lyon; 2018. Available from: http://www.theses.fr/2018LYSES035


University of Lund

22. Anderson, Rachele. Statistical inference and time-frequency estimation for non-stationary signal classification.

Degree: 2019, University of Lund

 This thesis focuses on statistical methods for non-stationary signals. The methods considered or developed address problems of stochastic modeling, inference, spectral analysis, time-frequency analysis, and… (more)

Subjects/Keywords: Sannolikhetsteori och statistik; Non-stationary processes; stochastic modeling; inference; spectral analysis; time-frequency analysis; classification; biomedical applications; deep learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Anderson, R. (2019). Statistical inference and time-frequency estimation for non-stationary signal classification. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/c70ed88e-fb95-4aa5-9476-c5e791a83a68 ; http://portal.research.lu.se/ws/files/69282270/Rachele_Anderson_kappa.pdf

Chicago Manual of Style (16th Edition):

Anderson, Rachele. “Statistical inference and time-frequency estimation for non-stationary signal classification.” 2019. Doctoral Dissertation, University of Lund. Accessed October 16, 2019. http://lup.lub.lu.se/record/c70ed88e-fb95-4aa5-9476-c5e791a83a68 ; http://portal.research.lu.se/ws/files/69282270/Rachele_Anderson_kappa.pdf.

MLA Handbook (7th Edition):

Anderson, Rachele. “Statistical inference and time-frequency estimation for non-stationary signal classification.” 2019. Web. 16 Oct 2019.

Vancouver:

Anderson R. Statistical inference and time-frequency estimation for non-stationary signal classification. [Internet] [Doctoral dissertation]. University of Lund; 2019. [cited 2019 Oct 16]. Available from: http://lup.lub.lu.se/record/c70ed88e-fb95-4aa5-9476-c5e791a83a68 ; http://portal.research.lu.se/ws/files/69282270/Rachele_Anderson_kappa.pdf.

Council of Science Editors:

Anderson R. Statistical inference and time-frequency estimation for non-stationary signal classification. [Doctoral Dissertation]. University of Lund; 2019. Available from: http://lup.lub.lu.se/record/c70ed88e-fb95-4aa5-9476-c5e791a83a68 ; http://portal.research.lu.se/ws/files/69282270/Rachele_Anderson_kappa.pdf


EPFL

23. Pu, Li. Relational Learning with Hypergraphs.

Degree: 2013, EPFL

Subjects/Keywords: relational learning; hypergraph learning; spectral graph theory; recommender system; network traffic inspection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Pu, L. (2013). Relational Learning with Hypergraphs. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/187371

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):

Pu, Li. “Relational Learning with Hypergraphs.” 2013. Thesis, EPFL. Accessed October 16, 2019. http://infoscience.epfl.ch/record/187371.

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

MLA Handbook (7th Edition):

Pu, Li. “Relational Learning with Hypergraphs.” 2013. Web. 16 Oct 2019.

Vancouver:

Pu L. Relational Learning with Hypergraphs. [Internet] [Thesis]. EPFL; 2013. [cited 2019 Oct 16]. Available from: http://infoscience.epfl.ch/record/187371.

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

Council of Science Editors:

Pu L. Relational Learning with Hypergraphs. [Thesis]. EPFL; 2013. Available from: http://infoscience.epfl.ch/record/187371

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

24. Perraudin, Nathanaël. Graph-based structures in data science: fundamental limits and applications to machine learning.

Degree: 2017, EPFL

 State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal… (more)

Subjects/Keywords: graph signal processing; semi-supervised learning; graph stationarity; graph uncertainty principle; local uncertainty principle; machine learning; data science; manifold regularization; structural clustering; spectral graph theory

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Perraudin, N. (2017). Graph-based structures in data science: fundamental limits and applications to machine learning. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/227982

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):

Perraudin, Nathanaël. “Graph-based structures in data science: fundamental limits and applications to machine learning.” 2017. Thesis, EPFL. Accessed October 16, 2019. http://infoscience.epfl.ch/record/227982.

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

MLA Handbook (7th Edition):

Perraudin, Nathanaël. “Graph-based structures in data science: fundamental limits and applications to machine learning.” 2017. Web. 16 Oct 2019.

Vancouver:

Perraudin N. Graph-based structures in data science: fundamental limits and applications to machine learning. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Oct 16]. Available from: http://infoscience.epfl.ch/record/227982.

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

Council of Science Editors:

Perraudin N. Graph-based structures in data science: fundamental limits and applications to machine learning. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/227982

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


University of Western Ontario

25. Shu, Lei. Automatic Image Classification for Planetary Exploration.

Degree: 2018, University of Western Ontario

 Autonomous techniques in the context of planetary exploration can maximize scientific return and reduce the need for human involvement. This thesis work studies two main… (more)

Subjects/Keywords: autonomous techniques; planetary exploration; rock image classification; unsuper- vised feature learning; self-taught learning; hyperspectral image classification; spatial-spectral features; support vector machine (SVM); Computer Engineering; Geological Engineering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shu, L. (2018). Automatic Image Classification for Planetary Exploration. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/5705

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):

Shu, Lei. “Automatic Image Classification for Planetary Exploration.” 2018. Thesis, University of Western Ontario. Accessed October 16, 2019. https://ir.lib.uwo.ca/etd/5705.

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

MLA Handbook (7th Edition):

Shu, Lei. “Automatic Image Classification for Planetary Exploration.” 2018. Web. 16 Oct 2019.

Vancouver:

Shu L. Automatic Image Classification for Planetary Exploration. [Internet] [Thesis]. University of Western Ontario; 2018. [cited 2019 Oct 16]. Available from: https://ir.lib.uwo.ca/etd/5705.

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

Council of Science Editors:

Shu L. Automatic Image Classification for Planetary Exploration. [Thesis]. University of Western Ontario; 2018. Available from: https://ir.lib.uwo.ca/etd/5705

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


Curtin University of Technology

26. Scott, Donald E. Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles .

Degree: 2009, Curtin University of Technology

 This study was a 360 degree exploration of the effectiveness of online learning experiences facilitated via Voice-over-Internet-Protocol (VoIP) by incorporating the insights afforded by students,… (more)

Subjects/Keywords: multiple intelligences (Gardner's theory); institutional rationale; Voice-over-Internet-Protocol (VoIP); university students; online learning experiences; learning management styles (Lessem's Spectral Management theory)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Scott, D. E. (2009). Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles . (Thesis). Curtin University of Technology. Retrieved from http://hdl.handle.net/20.500.11937/1064

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):

Scott, Donald E. “Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles .” 2009. Thesis, Curtin University of Technology. Accessed October 16, 2019. http://hdl.handle.net/20.500.11937/1064.

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

MLA Handbook (7th Edition):

Scott, Donald E. “Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles .” 2009. Web. 16 Oct 2019.

Vancouver:

Scott DE. Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles . [Internet] [Thesis]. Curtin University of Technology; 2009. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/20.500.11937/1064.

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

Council of Science Editors:

Scott DE. Effective online learning experiences: exploring potential relationships between Voice-over-Internet-Protocol (VoIP) learning environments and adult learners’ motivation, multiple intelligences, and learning styles . [Thesis]. Curtin University of Technology; 2009. Available from: http://hdl.handle.net/20.500.11937/1064

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

27. Nian, Ke. Unsupervised Spectral Ranking For Anomaly Detection.

Degree: 2014, University of Waterloo

 Anomaly detection is the problem of finding deviations from expected normal patterns. A wide variety of applications, such as fraud detection for credit cards and… (more)

Subjects/Keywords: Spectral Clustering; Anomaly Detection; Unsupervised Learning; Machine Learning

…time consuming, and highly inefficient. Data mining and machine learning techniques have been… …losses. Based on the type of input data used, data mining and machine learning techniques used… …for anomaly detection can be classified into three categories: • Supervised learning… …already known. The objective of the supervised learning is to learn a function that map the… …supervised learning 1 tasks include classification problems where the output targets are in… 

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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Nian, K. (2014). Unsupervised Spectral Ranking For Anomaly Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8782

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):

Nian, Ke. “Unsupervised Spectral Ranking For Anomaly Detection.” 2014. Thesis, University of Waterloo. Accessed October 16, 2019. http://hdl.handle.net/10012/8782.

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

MLA Handbook (7th Edition):

Nian, Ke. “Unsupervised Spectral Ranking For Anomaly Detection.” 2014. Web. 16 Oct 2019.

Vancouver:

Nian K. Unsupervised Spectral Ranking For Anomaly Detection. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10012/8782.

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

Council of Science Editors:

Nian K. Unsupervised Spectral Ranking For Anomaly Detection. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8782

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


Indian Institute of Science

28. Bhattacharya, Sourangshu. Computational Protein Structure Analysis : Kernel And Spectral Methods.

Degree: 2008, Indian Institute of Science

 The focus of this thesis is to develop computational techniques for analysis of protein structures. We model protein structures as points in 3-dimensional space which… (more)

Subjects/Keywords: Protein - Structure; Protein Structure - Data Processing; Protein Structure Alignment; Kernel Method; Structural Bioinformatics; Spectral Graph Theory; Machine Learning; Neighborhood Alignments; Structural Alignment; Protein Structure Classification; Bioinformatics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Bhattacharya, S. (2008). Computational Protein Structure Analysis : Kernel And Spectral Methods. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/831

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):

Bhattacharya, Sourangshu. “Computational Protein Structure Analysis : Kernel And Spectral Methods.” 2008. Thesis, Indian Institute of Science. Accessed October 16, 2019. http://hdl.handle.net/2005/831.

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

MLA Handbook (7th Edition):

Bhattacharya, Sourangshu. “Computational Protein Structure Analysis : Kernel And Spectral Methods.” 2008. Web. 16 Oct 2019.

Vancouver:

Bhattacharya S. Computational Protein Structure Analysis : Kernel And Spectral Methods. [Internet] [Thesis]. Indian Institute of Science; 2008. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/2005/831.

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

Council of Science Editors:

Bhattacharya S. Computational Protein Structure Analysis : Kernel And Spectral Methods. [Thesis]. Indian Institute of Science; 2008. Available from: http://hdl.handle.net/2005/831

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


Wright State University

29. Karvir, Hrishikesh. Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications.

Degree: PhD, Engineering PhD, 2010, Wright State University

  We integrated a sensor hardware test-bed using scientific grade, commercial off-the-shelf (COTS) technology and developed supporting software to enable rapid prototyping. The validity of… (more)

Subjects/Keywords: Engineering; Remote Sensing; Scientific Imaging; Systems Design; Machine learning; Pattern classification; Target detection; Convex hull; Piecewise linear classifier; Image registration; Systems integration; Multi-sensor fusion; Multi-spectral imaging

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Karvir, H. (2010). Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291

Chicago Manual of Style (16th Edition):

Karvir, Hrishikesh. “Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications.” 2010. Doctoral Dissertation, Wright State University. Accessed October 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291.

MLA Handbook (7th Edition):

Karvir, Hrishikesh. “Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications.” 2010. Web. 16 Oct 2019.

Vancouver:

Karvir H. Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications. [Internet] [Doctoral dissertation]. Wright State University; 2010. [cited 2019 Oct 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291.

Council of Science Editors:

Karvir H. Design and Validation of a Sensor Integration and Feature Fusion Test-Bed for Image-Based Pattern Recognition Applications. [Doctoral Dissertation]. Wright State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1291753291


Universitat Pompeu Fabra

30. Derkach, Dmytro. Spectrum analysis methods for 3D facial expression recognition and head pose estimation.

Degree: Departament de Tecnologies de la Informació i les Comunicacions, 2018, Universitat Pompeu Fabra

 Facial analysis has attracted considerable research efforts over the last decades, with a growing interest in improving the interaction and cooperation between people and computers.… (more)

Subjects/Keywords: Facial expression recognition; 3D face; Spectral shape analysis; Laplace operators; 3D head pose; Manifold learning; Tensor decomposition; Non-linear manifold modeling; 62

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Derkach, D. (2018). Spectrum analysis methods for 3D facial expression recognition and head pose estimation. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/664578

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):

Derkach, Dmytro. “Spectrum analysis methods for 3D facial expression recognition and head pose estimation.” 2018. Thesis, Universitat Pompeu Fabra. Accessed October 16, 2019. http://hdl.handle.net/10803/664578.

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

MLA Handbook (7th Edition):

Derkach, Dmytro. “Spectrum analysis methods for 3D facial expression recognition and head pose estimation.” 2018. Web. 16 Oct 2019.

Vancouver:

Derkach D. Spectrum analysis methods for 3D facial expression recognition and head pose estimation. [Internet] [Thesis]. Universitat Pompeu Fabra; 2018. [cited 2019 Oct 16]. Available from: http://hdl.handle.net/10803/664578.

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

Council of Science Editors:

Derkach D. Spectrum analysis methods for 3D facial expression recognition and head pose estimation. [Thesis]. Universitat Pompeu Fabra; 2018. Available from: http://hdl.handle.net/10803/664578

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

[1] [2]

.