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You searched for subject:(multiple kernel learning). Showing records 1 – 26 of 26 total matches.

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University of Illinois – Chicago

1. Esmaeilpourcharandabi, Sepideh. Kernel Learning for Structured Multi-View Data.

Degree: 2017, University of Illinois – Chicago

 It frequently happens in machine learning problems that the information explaining the subject of interest to be obtained from different sources or modalities and many… (more)

Subjects/Keywords: Structured Data; Multi-View Learning; Multiple Kernel Learning; Tensor Analysis

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APA (6th Edition):

Esmaeilpourcharandabi, S. (2017). Kernel Learning for Structured Multi-View Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21936

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):

Esmaeilpourcharandabi, Sepideh. “Kernel Learning for Structured Multi-View Data.” 2017. Thesis, University of Illinois – Chicago. Accessed October 22, 2019. http://hdl.handle.net/10027/21936.

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

MLA Handbook (7th Edition):

Esmaeilpourcharandabi, Sepideh. “Kernel Learning for Structured Multi-View Data.” 2017. Web. 22 Oct 2019.

Vancouver:

Esmaeilpourcharandabi S. Kernel Learning for Structured Multi-View Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10027/21936.

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

Council of Science Editors:

Esmaeilpourcharandabi S. Kernel Learning for Structured Multi-View Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21936

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


University of Alberta

2. Nilufar, Sharmin. Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis.

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

 Scale-space representation for an image is a significant way to generate features for object detection/classification. The size of the object we are looking for as… (more)

Subjects/Keywords: scale space; multiple kernel learning; oil sand image analysis

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APA (6th Edition):

Nilufar, S. (2011). Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/wh246t39w

Chicago Manual of Style (16th Edition):

Nilufar, Sharmin. “Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis.” 2011. Doctoral Dissertation, University of Alberta. Accessed October 22, 2019. https://era.library.ualberta.ca/files/wh246t39w.

MLA Handbook (7th Edition):

Nilufar, Sharmin. “Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis.” 2011. Web. 22 Oct 2019.

Vancouver:

Nilufar S. Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis. [Internet] [Doctoral dissertation]. University of Alberta; 2011. [cited 2019 Oct 22]. Available from: https://era.library.ualberta.ca/files/wh246t39w.

Council of Science Editors:

Nilufar S. Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis. [Doctoral Dissertation]. University of Alberta; 2011. Available from: https://era.library.ualberta.ca/files/wh246t39w


University of Alberta

3. Afkanpour, Arash. Multiple Kernel Learning with Many Kernels.

Degree: PhD, Department of Computing Science, 2013, University of Alberta

Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space,… (more)

Subjects/Keywords: Greedy Coordinate Descent; Multiple Kernel Learning; Stochastic Gradient Descent

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APA (6th Edition):

Afkanpour, A. (2013). Multiple Kernel Learning with Many Kernels. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/2z10wr20m

Chicago Manual of Style (16th Edition):

Afkanpour, Arash. “Multiple Kernel Learning with Many Kernels.” 2013. Doctoral Dissertation, University of Alberta. Accessed October 22, 2019. https://era.library.ualberta.ca/files/2z10wr20m.

MLA Handbook (7th Edition):

Afkanpour, Arash. “Multiple Kernel Learning with Many Kernels.” 2013. Web. 22 Oct 2019.

Vancouver:

Afkanpour A. Multiple Kernel Learning with Many Kernels. [Internet] [Doctoral dissertation]. University of Alberta; 2013. [cited 2019 Oct 22]. Available from: https://era.library.ualberta.ca/files/2z10wr20m.

Council of Science Editors:

Afkanpour A. Multiple Kernel Learning with Many Kernels. [Doctoral Dissertation]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/2z10wr20m


University of Louisville

4. Baili, Naouel. Unsupervised and semi-supervised fuzzy clustering with multiple kernels.

Degree: PhD, 2013, University of Louisville

  For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in… (more)

Subjects/Keywords: Clustering; Multiple kernel learning; Data mining; Semi-supervised learning; Pattern recognition; Image database categorization

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APA (6th Edition):

Baili, N. (2013). Unsupervised and semi-supervised fuzzy clustering with multiple kernels. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/61 ; https://ir.library.louisville.edu/etd/61

Chicago Manual of Style (16th Edition):

Baili, Naouel. “Unsupervised and semi-supervised fuzzy clustering with multiple kernels.” 2013. Doctoral Dissertation, University of Louisville. Accessed October 22, 2019. 10.18297/etd/61 ; https://ir.library.louisville.edu/etd/61.

MLA Handbook (7th Edition):

Baili, Naouel. “Unsupervised and semi-supervised fuzzy clustering with multiple kernels.” 2013. Web. 22 Oct 2019.

Vancouver:

Baili N. Unsupervised and semi-supervised fuzzy clustering with multiple kernels. [Internet] [Doctoral dissertation]. University of Louisville; 2013. [cited 2019 Oct 22]. Available from: 10.18297/etd/61 ; https://ir.library.louisville.edu/etd/61.

Council of Science Editors:

Baili N. Unsupervised and semi-supervised fuzzy clustering with multiple kernels. [Doctoral Dissertation]. University of Louisville; 2013. Available from: 10.18297/etd/61 ; https://ir.library.louisville.edu/etd/61


University of New South Wales

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

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

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Dang, Shaobo. “Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data.” 2018. Doctoral Dissertation, University of New South Wales. Accessed October 22, 2019. http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true.

MLA Handbook (7th Edition):

Dang, Shaobo. “Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data.” 2018. Web. 22 Oct 2019.

Vancouver:

Dang S. Learning-Based Methods for Outlier Detection in Imbalanced and Heterogeneous Data. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2019 Oct 22]. Available from: http://handle.unsw.edu.au/1959.4/59789 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:49734/SOURCE02?view=true.

Council of Science Editors:

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


NSYSU

6. Huang, Chi-wei. A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting.

Degree: Master, Electrical Engineering, 2009, NSYSU

 Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually the hyperparameters of the kernel functions.… (more)

Subjects/Keywords: SMO; multiple-kernel learning; support vector regression; Stock market forecasting; gradient projection

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APA (6th Edition):

Huang, C. (2009). A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0805109-121651

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, Chi-wei. “A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting.” 2009. Thesis, NSYSU. Accessed October 22, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0805109-121651.

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

MLA Handbook (7th Edition):

Huang, Chi-wei. “A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting.” 2009. Web. 22 Oct 2019.

Vancouver:

Huang C. A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting. [Internet] [Thesis]. NSYSU; 2009. [cited 2019 Oct 22]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0805109-121651.

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

Council of Science Editors:

Huang C. A Multiple-Kernel Support Vector Regression Approach for Stock Market Price Forecasting. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0805109-121651

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


Michigan Technological University

7. Pinar, Tony. Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals.

Degree: PhD, Department of Electrical and Computer Engineering, 2017, Michigan Technological University

  The work collected in this dissertation addresses the problem of data fusion. In other words, this is the problem of making decisions (also known… (more)

Subjects/Keywords: multiple kernel learning; fuzzy integrals; fuzzy measure; data fusion; Other Electrical and Computer Engineering

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APA (6th Edition):

Pinar, T. (2017). Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals. (Doctoral Dissertation). Michigan Technological University. Retrieved from http://digitalcommons.mtu.edu/etdr/375

Chicago Manual of Style (16th Edition):

Pinar, Tony. “Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals.” 2017. Doctoral Dissertation, Michigan Technological University. Accessed October 22, 2019. http://digitalcommons.mtu.edu/etdr/375.

MLA Handbook (7th Edition):

Pinar, Tony. “Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals.” 2017. Web. 22 Oct 2019.

Vancouver:

Pinar T. Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals. [Internet] [Doctoral dissertation]. Michigan Technological University; 2017. [cited 2019 Oct 22]. Available from: http://digitalcommons.mtu.edu/etdr/375.

Council of Science Editors:

Pinar T. Feature and Decision Level Fusion Using Multiple Kernel Learning and Fuzzy Integrals. [Doctoral Dissertation]. Michigan Technological University; 2017. Available from: http://digitalcommons.mtu.edu/etdr/375


George Mason University

8. Millis, David Howard. Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis .

Degree: 2014, George Mason University

 Gene prioritization is the process of ranking a list of candidate genes such that the genes that are most likely involved in a biological process… (more)

Subjects/Keywords: Bioinformatics; bioinformatics; functional enrichment; gene clustering; gene prioritization; multiple kernel learning; support vector machines

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APA (6th Edition):

Millis, D. H. (2014). Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8892

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):

Millis, David Howard. “Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis .” 2014. Thesis, George Mason University. Accessed October 22, 2019. http://hdl.handle.net/1920/8892.

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

MLA Handbook (7th Edition):

Millis, David Howard. “Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis .” 2014. Web. 22 Oct 2019.

Vancouver:

Millis DH. Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis . [Internet] [Thesis]. George Mason University; 2014. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/1920/8892.

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

Council of Science Editors:

Millis DH. Multiple Kernel Learning for Gene Prioritization, Clustering, and Functional Enrichment Analysis . [Thesis]. George Mason University; 2014. Available from: http://hdl.handle.net/1920/8892

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

9. Zhang, Xiao. Facial Expression Analysis via Transfer Learning.

Degree: PhD, Computer Engineering, 2015, U of Denver

  Automated analysis of facial expressions has remained an interesting and challenging research topic in the field of computer vision and pattern recognition due to… (more)

Subjects/Keywords: action unit detection; facial expression recognition; multiple kernel learning; multi-task learning; support vector machines; transfer learning

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APA (6th Edition):

Zhang, X. (2015). Facial Expression Analysis via Transfer Learning. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/731

Chicago Manual of Style (16th Edition):

Zhang, Xiao. “Facial Expression Analysis via Transfer Learning.” 2015. Doctoral Dissertation, U of Denver. Accessed October 22, 2019. https://digitalcommons.du.edu/etd/731.

MLA Handbook (7th Edition):

Zhang, Xiao. “Facial Expression Analysis via Transfer Learning.” 2015. Web. 22 Oct 2019.

Vancouver:

Zhang X. Facial Expression Analysis via Transfer Learning. [Internet] [Doctoral dissertation]. U of Denver; 2015. [cited 2019 Oct 22]. Available from: https://digitalcommons.du.edu/etd/731.

Council of Science Editors:

Zhang X. Facial Expression Analysis via Transfer Learning. [Doctoral Dissertation]. U of Denver; 2015. Available from: https://digitalcommons.du.edu/etd/731

10. Tzortzis, Grigorios. Clustering using similarity and kernel matrices.

Degree: 2014, University of Ioannina; Πανεπιστήμιο Ιωαννίνων

 This thesis studies the (unsupervised) clustering problem, which aims at partitioning a dataset into groups, called clusters, such that instances falling in the same cluster… (more)

Subjects/Keywords: Μηχανική μάθηση; Ομαδοποίηση; Μάθηση με πολλαπλές όψεις; Μάθηση με πολλαπλούς πυρήνες; Machine learning; Clustering; Multi-view learning; Multiple kernel learning

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APA (6th Edition):

Tzortzis, G. (2014). Clustering using similarity and kernel matrices. (Thesis). University of Ioannina; Πανεπιστήμιο Ιωαννίνων. Retrieved from http://hdl.handle.net/10442/hedi/37387

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):

Tzortzis, Grigorios. “Clustering using similarity and kernel matrices.” 2014. Thesis, University of Ioannina; Πανεπιστήμιο Ιωαννίνων. Accessed October 22, 2019. http://hdl.handle.net/10442/hedi/37387.

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

MLA Handbook (7th Edition):

Tzortzis, Grigorios. “Clustering using similarity and kernel matrices.” 2014. Web. 22 Oct 2019.

Vancouver:

Tzortzis G. Clustering using similarity and kernel matrices. [Internet] [Thesis]. University of Ioannina; Πανεπιστήμιο Ιωαννίνων; 2014. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10442/hedi/37387.

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

Council of Science Editors:

Tzortzis G. Clustering using similarity and kernel matrices. [Thesis]. University of Ioannina; Πανεπιστήμιο Ιωαννίνων; 2014. Available from: http://hdl.handle.net/10442/hedi/37387

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


University of Guelph

11. Xu, Tao. Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition .

Degree: 2017, University of Guelph

 In multiple instance learning (MIL), a class label is assigned to a collection (called bag) of instances instead of the individual instances. To learn concepts… (more)

Subjects/Keywords: multiple instance learning; concept learning; partial entropy; adaptive kernel diverse density estimate; common subgraph modeling; common random subgraph; Research Subject Categories

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APA (6th Edition):

Xu, T. (2017). Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/10405

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):

Xu, Tao. “Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition .” 2017. Thesis, University of Guelph. Accessed October 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/10405.

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

MLA Handbook (7th Edition):

Xu, Tao. “Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition .” 2017. Web. 22 Oct 2019.

Vancouver:

Xu T. Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition . [Internet] [Thesis]. University of Guelph; 2017. [cited 2019 Oct 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/10405.

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

Council of Science Editors:

Xu T. Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition . [Thesis]. University of Guelph; 2017. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/10405

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


NSYSU

12. Su, Wen-pin. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.

Degree: Master, Electrical Engineering, 2008, NSYSU

 Finding an efficient method to detect counterfeit banknotes is imperative. In this study, we propose multiple kernel weighted support vector machine for counterfeit banknote recognition.… (more)

Subjects/Keywords: Support vector machine; Banknote recognition; Multiple kernel learning; Semidefinite programming; False alarm rate; Weighted support vector machine

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APA (6th Edition):

Su, W. (2008). Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006

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):

Su, Wen-pin. “Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.” 2008. Thesis, NSYSU. Accessed October 22, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006.

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

MLA Handbook (7th Edition):

Su, Wen-pin. “Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition.” 2008. Web. 22 Oct 2019.

Vancouver:

Su W. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. [Internet] [Thesis]. NSYSU; 2008. [cited 2019 Oct 22]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006.

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

Council of Science Editors:

Su W. Employing Multiple Kernel Support Vector Machines for Counterfeit Banknote Recognition. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729108-172006

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


Brno University of Technology

13. Behúň, Kamil. Příznaky z videa pro klasifikaci .

Degree: 2013, Brno University of Technology

 Tato práce porovnává ručně-navrženy příznaky s příznaky naučenými metodami učení příznaků při klasifikací videa. Příznaky naučené pomocí Analýzy nezávislých podprostorů, Řídkými Autoenkodéry a vybělením Analýzou… (more)

Subjects/Keywords: Klasifikace videa; video příznaky; učení příznaků; Analýza hlavních komponent; Nezávislá analýza podprostoru; Řídké Autoenkodéry; Bag of Words; Support Vector Machine; Multiple Kernel Learning; Video Classification; video features; feature learning; Principal component analysis; Independent subspace analysis; Sparse Autoencoders; Bag of Words; Support Vector Machine; Multiple Kernel Learning

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APA (6th Edition):

Behúň, K. (2013). Příznaky z videa pro klasifikaci . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/53510

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):

Behúň, Kamil. “Příznaky z videa pro klasifikaci .” 2013. Thesis, Brno University of Technology. Accessed October 22, 2019. http://hdl.handle.net/11012/53510.

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

MLA Handbook (7th Edition):

Behúň, Kamil. “Příznaky z videa pro klasifikaci .” 2013. Web. 22 Oct 2019.

Vancouver:

Behúň K. Příznaky z videa pro klasifikaci . [Internet] [Thesis]. Brno University of Technology; 2013. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/11012/53510.

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

Council of Science Editors:

Behúň K. Příznaky z videa pro klasifikaci . [Thesis]. Brno University of Technology; 2013. Available from: http://hdl.handle.net/11012/53510

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


EPFL

14. Luo, Jie. Open-Ended Learning of Visual and Multi-Modal Patterns.

Degree: 2011, EPFL

 A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in… (more)

Subjects/Keywords: machine learning; multiple cue integration; visual recognition; online learning; multiple kernel learning; weakly supervised learning; transfer learning; apprentissage automatique; intégration de plusieurs primitives; reconnaissance visuelle; apprentissage en ligne; apprentissage par noyaux multiples; apprentissage faiblement supervisé; transfert de connaissances

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APA (6th Edition):

Luo, J. (2011). Open-Ended Learning of Visual and Multi-Modal Patterns. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/169610

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):

Luo, Jie. “Open-Ended Learning of Visual and Multi-Modal Patterns.” 2011. Thesis, EPFL. Accessed October 22, 2019. http://infoscience.epfl.ch/record/169610.

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

MLA Handbook (7th Edition):

Luo, Jie. “Open-Ended Learning of Visual and Multi-Modal Patterns.” 2011. Web. 22 Oct 2019.

Vancouver:

Luo J. Open-Ended Learning of Visual and Multi-Modal Patterns. [Internet] [Thesis]. EPFL; 2011. [cited 2019 Oct 22]. Available from: http://infoscience.epfl.ch/record/169610.

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

Council of Science Editors:

Luo J. Open-Ended Learning of Visual and Multi-Modal Patterns. [Thesis]. EPFL; 2011. Available from: http://infoscience.epfl.ch/record/169610

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

15. Tian, Xilan. Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces.

Degree: Docteur es, Informatique, 2012, Rouen, INSA

Les Interfaces Cerveau-Machine (ICM) ont été appliquées avec succès aussi bien dans le domaine clinique que pour l'amélioration de la vie quotidienne de patients avec… (more)

Subjects/Keywords: Interface cerveau-machine; Apprentissage multi-noyaux; Apprentissage semi-supervisé; TSVM-MKL; LaMKL; Émotionnel ICM; Brain-computer Interface; Multiple kernel learning; Semi-supervised learning; TSVM-MKL; LaMKL; Emotional BCI

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

APA (6th Edition):

Tian, X. (2012). Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces. (Doctoral Dissertation). Rouen, INSA. Retrieved from http://www.theses.fr/2012ISAM0003

Chicago Manual of Style (16th Edition):

Tian, Xilan. “Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces.” 2012. Doctoral Dissertation, Rouen, INSA. Accessed October 22, 2019. http://www.theses.fr/2012ISAM0003.

MLA Handbook (7th Edition):

Tian, Xilan. “Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces.” 2012. Web. 22 Oct 2019.

Vancouver:

Tian X. Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces. [Internet] [Doctoral dissertation]. Rouen, INSA; 2012. [cited 2019 Oct 22]. Available from: http://www.theses.fr/2012ISAM0003.

Council of Science Editors:

Tian X. Apprentissage et noyau pour les interfaces cerveau-machine : Study of kernel machines towards brain-computer interfaces. [Doctoral Dissertation]. Rouen, INSA; 2012. Available from: http://www.theses.fr/2012ISAM0003

16. Castro Witting, Eduardo Jose. Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization.

Degree: Electrical and Computer Engineering, 2014, University of New Mexico

 Mental disorders are diagnosed on the basis of reported symptoms and externally observed clinical signs. Nonetheless, these cannot be evaluated by means of clinical tests.… (more)

Subjects/Keywords: multiple kernel learning; schizophrenia; machine learning; fMRI; medical imaging; SVM

…composite kernels (RCK) and ν-multiple kernel learning (ν-MKL). This work… …presents a machine learning framework based on a multiple-kernel data representation to… …of multiple kernel learning algorithms. Chapter 3 introduces the proposed machine learning… …Calhoun, “A multiple kernel learning approach for schizophrenia classification from complex… …D. Calhoun, “A multiple kernel learning approach to perform classification of groups from… 

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APA (6th Edition):

Castro Witting, E. J. (2014). Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/23542

Chicago Manual of Style (16th Edition):

Castro Witting, Eduardo Jose. “Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization.” 2014. Doctoral Dissertation, University of New Mexico. Accessed October 22, 2019. http://hdl.handle.net/1928/23542.

MLA Handbook (7th Edition):

Castro Witting, Eduardo Jose. “Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization.” 2014. Web. 22 Oct 2019.

Vancouver:

Castro Witting EJ. Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/1928/23542.

Council of Science Editors:

Castro Witting EJ. Application of Multiple Kernel Learning on Brain Imaging for Mental Illness Characterization. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/23542


Universitat Pompeu Fabra

17. Sánchez Martínez, Sergio. Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics.

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

 Esta tesis se centra en el desarrollo de herramientas de aprendizaje automático para mejorar la caracterización de la anatomía y la función cardíaca en el… (more)

Subjects/Keywords: Machine learning; Medical image analysis; Pattern recognition; Multiple kernel learning; Dimensionality reduction; Echocardiography; Early diagnosis; Heart failure; Cardiac resynchronization therapy; Aprendizaje automático; Análisis de imágenes médicas; Reconocimiento de patrones; Aprendizaje de kernel múltiple; Ecocardiografía; Diagnóstico temprano; Insuficiencia cardíaca; Tratamiento de re-sincronización cardíaca; 62

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APA (6th Edition):

Sánchez Martínez, S. (2018). Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/663748

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):

Sánchez Martínez, Sergio. “Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics.” 2018. Thesis, Universitat Pompeu Fabra. Accessed October 22, 2019. http://hdl.handle.net/10803/663748.

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

MLA Handbook (7th Edition):

Sánchez Martínez, Sergio. “Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics.” 2018. Web. 22 Oct 2019.

Vancouver:

Sánchez Martínez S. Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics. [Internet] [Thesis]. Universitat Pompeu Fabra; 2018. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10803/663748.

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

Council of Science Editors:

Sánchez Martínez S. Multi-feature machine learning analysis for an improved characterization of the cardiac mechanics. [Thesis]. Universitat Pompeu Fabra; 2018. Available from: http://hdl.handle.net/10803/663748

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


University of Ottawa

18. Wang, Xiaoguang. Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions .

Degree: 2015, University of Ottawa

 With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, the Internet, and finance, it becomes critical… (more)

Subjects/Keywords: Multiple-Instance Learning; Balanced Class Distributions; Skewed Class Distributions; Multi-View; Data Fusion; Multi-Kernel SVM; SMOTE; Cost-sensitive Boosting; Instance-weighted Boosting SVM

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APA (6th Edition):

Wang, X. (2015). Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/32184

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, Xiaoguang. “Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions .” 2015. Thesis, University of Ottawa. Accessed October 22, 2019. http://hdl.handle.net/10393/32184.

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

MLA Handbook (7th Edition):

Wang, Xiaoguang. “Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions .” 2015. Web. 22 Oct 2019.

Vancouver:

Wang X. Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions . [Internet] [Thesis]. University of Ottawa; 2015. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10393/32184.

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

Council of Science Editors:

Wang X. Design and Analysis of Techniques for Multiple-Instance Learning in the Presence of Balanced and Skewed Class Distributions . [Thesis]. University of Ottawa; 2015. Available from: http://hdl.handle.net/10393/32184

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

19. Doran, Gary Brian, Jr. Multiple-Instance Learning from Distributions.

Degree: PhD, EECS - Computer and Information Sciences, 2015, Case Western Reserve University

 I propose a new theoretical framework for analyzing the multiple-instance learning (MIL) setting. In MIL, training examples are provided to a learning algorithm in the… (more)

Subjects/Keywords: Computer Science; Artificial Intelligence; machine learning; multiple-instance learning; kernel methods; learning theory; classfiication

…on machine learning in general, but more specifically on how kernel methods might be… …multiple-instance learning. MILES Multiple-Instance Learning via Embedded instance Selection. MMD… …area, and lighting. sMIL sparse MIL. SMILe Shuffled Multiple-Instance Learning. SMILeSVM… …example; in the multiple-instance setting, an element contained within a bag. kernel a real… …concept. xvii Multiple-Instance Learning from Distributions Abstract by GARY DORAN I propose… 

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APA (6th Edition):

Doran, Gary Brian, J. (2015). Multiple-Instance Learning from Distributions. (Doctoral Dissertation). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923

Chicago Manual of Style (16th Edition):

Doran, Gary Brian, Jr. “Multiple-Instance Learning from Distributions.” 2015. Doctoral Dissertation, Case Western Reserve University. Accessed October 22, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923.

MLA Handbook (7th Edition):

Doran, Gary Brian, Jr. “Multiple-Instance Learning from Distributions.” 2015. Web. 22 Oct 2019.

Vancouver:

Doran, Gary Brian J. Multiple-Instance Learning from Distributions. [Internet] [Doctoral dissertation]. Case Western Reserve University; 2015. [cited 2019 Oct 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923.

Council of Science Editors:

Doran, Gary Brian J. Multiple-Instance Learning from Distributions. [Doctoral Dissertation]. Case Western Reserve University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1417736923

20. Saide, Chafic. Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing.

Degree: Docteur es, Optimisation et Sûreté des Systèmes, 2013, Troyes

L’estimation fonctionnelle basée sur les espaces de Hilbert à noyau reproduisant demeure un sujet de recherche actif pour l’identification des systèmes non linéaires. L'ordre du… (more)

Subjects/Keywords: Séries chronologiques; Filtres adaptatifs; Noyaux (analyse fonctionnelle); Hilbert, Espaces de; Apprentissage automatique; Systèmes à entrées multiples et à sorties multiples; Paliers magnétiques; Time series analysis; Adaptive filters; Kernel functions; Hilbert space; Machine learning; Multiple-input multiple-output systems; Magnetic bearing; 003

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APA (6th Edition):

Saide, C. (2013). Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing. (Doctoral Dissertation). Troyes. Retrieved from http://www.theses.fr/2013TROY0018

Chicago Manual of Style (16th Edition):

Saide, Chafic. “Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing.” 2013. Doctoral Dissertation, Troyes. Accessed October 22, 2019. http://www.theses.fr/2013TROY0018.

MLA Handbook (7th Edition):

Saide, Chafic. “Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing.” 2013. Web. 22 Oct 2019.

Vancouver:

Saide C. Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing. [Internet] [Doctoral dissertation]. Troyes; 2013. [cited 2019 Oct 22]. Available from: http://www.theses.fr/2013TROY0018.

Council of Science Editors:

Saide C. Filtrage adaptatif à l’aide de méthodes à noyau : application au contrôle d’un palier magnétique actif : Adaptive filtering using kernel methods : application to the control of an active magnetic bearing. [Doctoral Dissertation]. Troyes; 2013. Available from: http://www.theses.fr/2013TROY0018

21. Phoungphol, Piyaphol. A Classification Framework for Imbalanced Data.

Degree: PhD, Computer Science, 2013, Georgia State University

  As information technology advances, the demands for developing a reliable and highly accurate predictive model from many domains are increasing. Traditional classification algorithms can… (more)

Subjects/Keywords: Imbalanced Data; Privacy; Distributed Learning; Multiple Kernel Learning; Support Vector Machine; Feature Selection

Learning . . . . . . . . . . . . . . . . . . . . . . . 60 7.2.2 Generalized Multiple Kernel… …Vector Machine • DGMKL - Distributed Generalized Multiple Kernel Learning • FP - False Positive… …FN - False Negative • G-mean - Geometric mean • GMKL - Generalized Multiple Kernel Learning… …high-dimensional data. The extended framework applied Generalized Multiple Kernel Learning… …Generalized Multiple Kernel . . . . . . . . . . . . . . . . . . . . . . 60 7.2.1 Multiple Kernel… 

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APA (6th Edition):

Phoungphol, P. (2013). A Classification Framework for Imbalanced Data. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/78

Chicago Manual of Style (16th Edition):

Phoungphol, Piyaphol. “A Classification Framework for Imbalanced Data.” 2013. Doctoral Dissertation, Georgia State University. Accessed October 22, 2019. https://scholarworks.gsu.edu/cs_diss/78.

MLA Handbook (7th Edition):

Phoungphol, Piyaphol. “A Classification Framework for Imbalanced Data.” 2013. Web. 22 Oct 2019.

Vancouver:

Phoungphol P. A Classification Framework for Imbalanced Data. [Internet] [Doctoral dissertation]. Georgia State University; 2013. [cited 2019 Oct 22]. Available from: https://scholarworks.gsu.edu/cs_diss/78.

Council of Science Editors:

Phoungphol P. A Classification Framework for Imbalanced Data. [Doctoral Dissertation]. Georgia State University; 2013. Available from: https://scholarworks.gsu.edu/cs_diss/78


Linköping University

22. Ahad, George Abo Al. Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets.

Degree: Production Economics, 2018, Linköping University

  Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging… (more)

Subjects/Keywords: Machine Learning; Finance; Financial Time Series; Support Vector Machines; Relevance Vector Machines; Multiple Kernel Learning; Simulated Annealing; SVM; RVM; MKL; SA; FSVM; TSVM; FTSVM; Other Engineering and Technologies not elsewhere specified; Övrig annan teknik

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APA (6th Edition):

Ahad, G. A. A. (2018). Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459

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):

Ahad, George Abo Al. “Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets.” 2018. Thesis, Linköping University. Accessed October 22, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.

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

MLA Handbook (7th Edition):

Ahad, George Abo Al. “Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets.” 2018. Web. 22 Oct 2019.

Vancouver:

Ahad GAA. Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets. [Internet] [Thesis]. Linköping University; 2018. [cited 2019 Oct 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.

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

Council of Science Editors:

Ahad GAA. Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets. [Thesis]. Linköping University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459

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

23. Gao, Huanhuan. Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications.

Degree: Docteur es, Mécanique Numérique : Unité de recherche en Mécanique - Laboratoire Roberval (FRE UTC - CNRS 2012), 2019, Compiègne; Université libre de Bruxelles (1970-....)

La thèse se concentre sur une recherche méthodologique sur l'optimisation structurelle catégorielle au moyen d'un apprentissage multiple. Dans cette thèse, les variables catégorielles non ordinales… (more)

Subjects/Keywords: Optimisation structurelle; Apprentissage multiple; Réduction de la dimensionnalité; Structure en treillis; Analyse en composantes principales pondérée; Categorical optimization; Structural optimization; Manifold learning; Dimensionality reduction; Polynomial fitting; Locally linear embedding; Isomap; K-manifolds learning; Evolutionary methods; Kernel functions; Polynomial fitting; Truss structure; Weighted principal component analysis

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APA (6th Edition):

Gao, H. (2019). Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications. (Doctoral Dissertation). Compiègne; Université libre de Bruxelles (1970-....). Retrieved from http://www.theses.fr/2019COMP2471

Chicago Manual of Style (16th Edition):

Gao, Huanhuan. “Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications.” 2019. Doctoral Dissertation, Compiègne; Université libre de Bruxelles (1970-....). Accessed October 22, 2019. http://www.theses.fr/2019COMP2471.

MLA Handbook (7th Edition):

Gao, Huanhuan. “Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications.” 2019. Web. 22 Oct 2019.

Vancouver:

Gao H. Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications. [Internet] [Doctoral dissertation]. Compiègne; Université libre de Bruxelles (1970-....); 2019. [cited 2019 Oct 22]. Available from: http://www.theses.fr/2019COMP2471.

Council of Science Editors:

Gao H. Categorical structural optimization : methods and applications : Optimisation structurelle catégorique : méthodes et applications. [Doctoral Dissertation]. Compiègne; Université libre de Bruxelles (1970-....); 2019. Available from: http://www.theses.fr/2019COMP2471


Universidad de Cantabria

24. Vaerenbergh, Steven Van. Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals.

Degree: Departamento de Ingeniería de Comunicaciones, 2010, Universidad de Cantabria

 In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of… (more)

Subjects/Keywords: spectral clustering; multiple-input multiple-output systems (MIMO); blind equalization of nonlinear systems; identification of nonlinear systems; signal processing; kernel methods; machine learning; análisis de correlaciones canónicas con kernels; separación ciega de fuentes post no lineal; filtrado adaptativo mediante Kernels; agrupamiento espectral; sistemas de múltiples entradas y múltiples salidas; igualación ciega de sistemas no lineales; identificación de sistemas no lineales; procesado de señal; métodos kernel; aprendizaje máquina; kernel adaptive filtering; postnonlinear blind source separation (BSS); adaptive kernel canonical correlation analysis; Teoría de la Señal y Comunicaciones; 512; 621.3

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APA (6th Edition):

Vaerenbergh, S. V. (2010). Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals. (Thesis). Universidad de Cantabria. Retrieved from http://hdl.handle.net/10803/10673

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):

Vaerenbergh, Steven Van. “Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals.” 2010. Thesis, Universidad de Cantabria. Accessed October 22, 2019. http://hdl.handle.net/10803/10673.

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

MLA Handbook (7th Edition):

Vaerenbergh, Steven Van. “Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals.” 2010. Web. 22 Oct 2019.

Vancouver:

Vaerenbergh SV. Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals. [Internet] [Thesis]. Universidad de Cantabria; 2010. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10803/10673.

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

Council of Science Editors:

Vaerenbergh SV. Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals. [Thesis]. Universidad de Cantabria; 2010. Available from: http://hdl.handle.net/10803/10673

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

25. Jun, Yang. Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos .

Degree: 2015, University of Ottawa

 We estimate the velocity field of the blood flow in a human face from videos. Our approach first performs spatial preprocessing to improve the signal-to-noise… (more)

Subjects/Keywords: Blood Flow; Velocity Field; PCA; DFT; Feature Extraction; KNN; Multiple Kernel Learning Classification; Denoising; Synthetic Video

…artifacts or non-variation noise positions. The multi-kernel based k-NN classification is applied… …Abdulmotaleb El Saddik. On the Learning of Image Social Relevance from Heterogeneous Social Network… …pattern recognition and machine learning, the k-Nearest Neighbors algorithm (k-NN) is… …of the simplest of all machine learning algorithms, the k-NN algorithm has advantages in… …crossvalidation[37]. 19 2.4.1 Classifying with distance measurements In machine learning… 

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APA (6th Edition):

Jun, Y. (2015). Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/32539

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):

Jun, Yang. “Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos .” 2015. Thesis, University of Ottawa. Accessed October 22, 2019. http://hdl.handle.net/10393/32539.

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

MLA Handbook (7th Edition):

Jun, Yang. “Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos .” 2015. Web. 22 Oct 2019.

Vancouver:

Jun Y. Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos . [Internet] [Thesis]. University of Ottawa; 2015. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/10393/32539.

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

Council of Science Editors:

Jun Y. Analysis and Visualization of the Two-Dimensional Blood Flow Velocity Field from Videos . [Thesis]. University of Ottawa; 2015. Available from: http://hdl.handle.net/10393/32539

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

26. Hwang, Sung Ju. Discriminative object categorization with external semantic knowledge.

Degree: PhD, Computer Science, 2013, University of Texas – Austin

 Visual object category recognition is one of the most challenging problems in computer vision. Even assuming that we can obtain a near-perfect instance level representation… (more)

Subjects/Keywords: Computer vision; Machine learning; Object categorization; Object recognition; Feature learning; Metric learning; Multitask learning; Multiple kernel learning; Embedding; Manifold learning; Regularization method; Structured sparsity; Structured regularization; Hierarchical model

…features . . . 2.2.3 Learning to combine features with multiple kernel learning xi 1 4 8 13 14… …combination of the kernels, such as multiple kernel learning [103], or LP-Boost [42… …different semantic granularities, and use multiple kernel learning (MKL) to learn the… …Learning a semantic kernel forest . . . . . . . . . . . . . 89 5.1.2 Learning class-specific… …learning techniques. In many cases, this could result in the recognition model being… 

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

APA (6th Edition):

Hwang, S. J. (2013). Discriminative object categorization with external semantic knowledge. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21320

Chicago Manual of Style (16th Edition):

Hwang, Sung Ju. “Discriminative object categorization with external semantic knowledge.” 2013. Doctoral Dissertation, University of Texas – Austin. Accessed October 22, 2019. http://hdl.handle.net/2152/21320.

MLA Handbook (7th Edition):

Hwang, Sung Ju. “Discriminative object categorization with external semantic knowledge.” 2013. Web. 22 Oct 2019.

Vancouver:

Hwang SJ. Discriminative object categorization with external semantic knowledge. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2013. [cited 2019 Oct 22]. Available from: http://hdl.handle.net/2152/21320.

Council of Science Editors:

Hwang SJ. Discriminative object categorization with external semantic knowledge. [Doctoral Dissertation]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21320

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