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You searched for subject:(feature extraction methods). Showing records 1 – 14 of 14 total matches.

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1. Izquierdo Verdiguier, Emma. Kernel Feature Extraction Methods for Remote Sensing Data Analysis.

Degree: 2018, TDX

 Technological advances in the last decades have improved our capabilities of collecting and storing high data volumes. However, this makes that in some fields, such… (more)

Subjects/Keywords: remote sensing; regression; feature extraction methods; invariances; clustering; generative kernels; image classification; UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO::Otras especialidades de la tierra, espacio o entorno

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

APA (6th Edition):

Izquierdo Verdiguier, E. (2018). Kernel Feature Extraction Methods for Remote Sensing Data Analysis. (Thesis). TDX. Retrieved from http://hdl.handle.net/10803/568718

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):

Izquierdo Verdiguier, Emma. “Kernel Feature Extraction Methods for Remote Sensing Data Analysis.” 2018. Thesis, TDX. Accessed November 13, 2019. http://hdl.handle.net/10803/568718.

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

MLA Handbook (7th Edition):

Izquierdo Verdiguier, Emma. “Kernel Feature Extraction Methods for Remote Sensing Data Analysis.” 2018. Web. 13 Nov 2019.

Vancouver:

Izquierdo Verdiguier E. Kernel Feature Extraction Methods for Remote Sensing Data Analysis. [Internet] [Thesis]. TDX; 2018. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/10803/568718.

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

Council of Science Editors:

Izquierdo Verdiguier E. Kernel Feature Extraction Methods for Remote Sensing Data Analysis. [Thesis]. TDX; 2018. Available from: http://hdl.handle.net/10803/568718

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


University of Southern California

2. Yang, Kiyoung. Multivariate time series analysis based on principal component analysis.

Degree: PhD, Computer Science, 2007, University of Southern California

 Time series is a series of observations over time. When there is one observation at each time instance, it is called a univariate time series… (more)

Subjects/Keywords: time series; principal component analysis; kernel methods; similarity measure; index structure; feature selection; feature extraction; stationarity; n-way analysis

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APA (6th Edition):

Yang, K. (2007). Multivariate time series analysis based on principal component analysis. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/529276/rec/4283

Chicago Manual of Style (16th Edition):

Yang, Kiyoung. “Multivariate time series analysis based on principal component analysis.” 2007. Doctoral Dissertation, University of Southern California. Accessed November 13, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/529276/rec/4283.

MLA Handbook (7th Edition):

Yang, Kiyoung. “Multivariate time series analysis based on principal component analysis.” 2007. Web. 13 Nov 2019.

Vancouver:

Yang K. Multivariate time series analysis based on principal component analysis. [Internet] [Doctoral dissertation]. University of Southern California; 2007. [cited 2019 Nov 13]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/529276/rec/4283.

Council of Science Editors:

Yang K. Multivariate time series analysis based on principal component analysis. [Doctoral Dissertation]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/529276/rec/4283


Universiteit Utrecht

3. Loog, Marco. Supervised dimensionality reduction and contextual pattern recognition in medical image processing.

Degree: 2004, Universiteit Utrecht

 The past few years have witnessed a significant increase in the number of supervised methods employed in diverse image processing tasks. Especially in medical image… (more)

Subjects/Keywords: Geneeskunde; image analysis; supervised methods; feature extraction; linear dimensionality reduction; black math; local methods; contextual methods; back-transduction; segmentation; regression

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

APA (6th Edition):

Loog, M. (2004). Supervised dimensionality reduction and contextual pattern recognition in medical image processing. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/7292

Chicago Manual of Style (16th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Doctoral Dissertation, Universiteit Utrecht. Accessed November 13, 2019. http://dspace.library.uu.nl:8080/handle/1874/7292.

MLA Handbook (7th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Web. 13 Nov 2019.

Vancouver:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2004. [cited 2019 Nov 13]. Available from: http://dspace.library.uu.nl:8080/handle/1874/7292.

Council of Science Editors:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Doctoral Dissertation]. Universiteit Utrecht; 2004. Available from: http://dspace.library.uu.nl:8080/handle/1874/7292

4. Loog, Marco. Supervised dimensionality reduction and contextual pattern recognition in medical image processing.

Degree: 2004, University Utrecht

 The past few years have witnessed a significant increase in the number of supervised methods employed in diverse image processing tasks. Especially in medical image… (more)

Subjects/Keywords: image analysis; supervised methods; feature extraction; linear dimensionality reduction; black math; local methods; contextual methods; back-transduction; segmentation; regression

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

APA (6th Edition):

Loog, M. (2004). Supervised dimensionality reduction and contextual pattern recognition in medical image processing. (Doctoral Dissertation). University Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292

Chicago Manual of Style (16th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Doctoral Dissertation, University Utrecht. Accessed November 13, 2019. http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292.

MLA Handbook (7th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Web. 13 Nov 2019.

Vancouver:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Internet] [Doctoral dissertation]. University Utrecht; 2004. [cited 2019 Nov 13]. Available from: http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292.

Council of Science Editors:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Doctoral Dissertation]. University Utrecht; 2004. Available from: http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292

5. Loog, Marco. Supervised dimensionality reduction and contextual pattern recognition in medical image processing.

Degree: 2004, University Utrecht

 The past few years have witnessed a significant increase in the number of supervised methods employed in diverse image processing tasks. Especially in medical image… (more)

Subjects/Keywords: image analysis; supervised methods; feature extraction; linear dimensionality reduction; black math; local methods; contextual methods; back-transduction; segmentation; regression

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

APA (6th Edition):

Loog, M. (2004). Supervised dimensionality reduction and contextual pattern recognition in medical image processing. (Doctoral Dissertation). University Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292

Chicago Manual of Style (16th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Doctoral Dissertation, University Utrecht. Accessed November 13, 2019. http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292.

MLA Handbook (7th Edition):

Loog, Marco. “Supervised dimensionality reduction and contextual pattern recognition in medical image processing.” 2004. Web. 13 Nov 2019.

Vancouver:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Internet] [Doctoral dissertation]. University Utrecht; 2004. [cited 2019 Nov 13]. Available from: http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292.

Council of Science Editors:

Loog M. Supervised dimensionality reduction and contextual pattern recognition in medical image processing. [Doctoral Dissertation]. University Utrecht; 2004. Available from: http://dspace.library.uu.nl/handle/1874/7292 ; URN:NBN:NL:UI:10-1874-7292 ; urn:isbn:90-393-3804-3 ; URN:NBN:NL:UI:10-1874-7292 ; http://dspace.library.uu.nl/handle/1874/7292

6. Ricci, Francesco. Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems.

Degree: 2018, CEUR-WS

Subjects/Keywords: Feature Selection; Decision making; Feature extraction; Context dependent; Context information; Context-aware recommender systems; Contextual factors; Contextual information; Experimental evaluation; Selection methods; System's performance; Recommender systems; Informática

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APA (6th Edition):

Ricci, F. (2018). Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems. (Thesis). CEUR-WS. Retrieved from http://hdl.handle.net/10486/675204

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):

Ricci, Francesco. “Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems.” 2018. Thesis, CEUR-WS. Accessed November 13, 2019. http://hdl.handle.net/10486/675204.

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

MLA Handbook (7th Edition):

Ricci, Francesco. “Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems.” 2018. Web. 13 Nov 2019.

Vancouver:

Ricci F. Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems. [Internet] [Thesis]. CEUR-WS; 2018. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/10486/675204.

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

Council of Science Editors:

Ricci F. Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems. [Thesis]. CEUR-WS; 2018. Available from: http://hdl.handle.net/10486/675204

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

7. Ghahramani, Zoubin. A Probabilistic Model for Dirty Multi-task Feature Selection.

Degree: 2018, International Machine Learning Society (IMLS)

Subjects/Keywords: Artificial intelligence; Benchmarking; Learning systems; Statistics; Approximate inference; Expectation; Propagation; Feature selection methods; Latent variable; Learning tasks; Model-based OPC; Predictive performance; Probabilistic modeling; Feature extraction; Informática

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APA (6th Edition):

Ghahramani, Z. (2018). A Probabilistic Model for Dirty Multi-task Feature Selection. (Thesis). International Machine Learning Society (IMLS). Retrieved from http://hdl.handle.net/10486/674892

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):

Ghahramani, Zoubin. “A Probabilistic Model for Dirty Multi-task Feature Selection.” 2018. Thesis, International Machine Learning Society (IMLS). Accessed November 13, 2019. http://hdl.handle.net/10486/674892.

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

MLA Handbook (7th Edition):

Ghahramani, Zoubin. “A Probabilistic Model for Dirty Multi-task Feature Selection.” 2018. Web. 13 Nov 2019.

Vancouver:

Ghahramani Z. A Probabilistic Model for Dirty Multi-task Feature Selection. [Internet] [Thesis]. International Machine Learning Society (IMLS); 2018. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/10486/674892.

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

Council of Science Editors:

Ghahramani Z. A Probabilistic Model for Dirty Multi-task Feature Selection. [Thesis]. International Machine Learning Society (IMLS); 2018. Available from: http://hdl.handle.net/10486/674892

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


Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

8. Βερβερίδης, Δημήτριος. Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων.

Degree: 2008, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ)

Τhe subject of this thesis is the recognition of emotions from speech. The investigation began with a review of methods that extract acoustic characteristics and… (more)

Subjects/Keywords: Αναγνώριση συναισθημάτων; Υπολογισμός ακουστικών χαρακτηριστικών; Μέθοδοι επιλογής χαρακτηριστικών; Μέθοδος διασταυρωμένης επικύρωσης; Μίγματα γκαουσιανών κατανομών; Συντελεστής πολλαπλής συσχέτισης; Εφίδρωση; Παλμοί καρδιάς; Εικονικό περιβάλλον σεισμού; Emotion recognition; Acoustic features extraction; Feature selection methods; Cross-validation; Gaussian Mixture Models; Multiple correlation coefficient; Sweat and heart beat rate; Virtual earthquake environment

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APA (6th Edition):

Βερβερίδης, . . (2008). Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων. (Thesis). Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Retrieved from http://hdl.handle.net/10442/hedi/18935

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):

Βερβερίδης, Δημήτριος. “Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων.” 2008. Thesis, Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ). Accessed November 13, 2019. http://hdl.handle.net/10442/hedi/18935.

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

MLA Handbook (7th Edition):

Βερβερίδης, Δημήτριος. “Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων.” 2008. Web. 13 Nov 2019.

Vancouver:

Βερβερίδης . Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων. [Internet] [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2008. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/10442/hedi/18935.

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

Council of Science Editors:

Βερβερίδης . Τεχνικές ψηφιακής επεξεργασίας ομιλίας στην αναγνώριση συναισθημάτων. [Thesis]. Aristotle University Of Thessaloniki (AUTH); Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (ΑΠΘ); 2008. Available from: http://hdl.handle.net/10442/hedi/18935

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

9. Izquierdo Verdiguier, Emma. Kernel Feature Extraction Methods for Remote Sensing Data Analysis .

Degree: 2014, Universitat de Valencia

 Technological advances in the last decades have improved our capabilities of collecting and storing high data volumes. However, this makes that in some fields, such… (more)

Subjects/Keywords: remote sensing; regression; feature extraction methods; invariances; clustering; generative kernels; image classification

…44 4.2 Comparison of supervised kernel feature extraction methods… …perspective. In particular, different kernel feature extraction methods are proposed to discover the… …this context, this Thesis presents different kernel feature extraction methods with two main… …whole dataset is proposed. The feature extraction methods proposed in this Thesis are… …methods to learn the nonlinear feature extraction transformation. X (a) Nonlinear f… 

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APA (6th Edition):

Izquierdo Verdiguier, E. (2014). Kernel Feature Extraction Methods for Remote Sensing Data Analysis . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/37288

Chicago Manual of Style (16th Edition):

Izquierdo Verdiguier, Emma. “Kernel Feature Extraction Methods for Remote Sensing Data Analysis .” 2014. Doctoral Dissertation, Universitat de Valencia. Accessed November 13, 2019. http://hdl.handle.net/10550/37288.

MLA Handbook (7th Edition):

Izquierdo Verdiguier, Emma. “Kernel Feature Extraction Methods for Remote Sensing Data Analysis .” 2014. Web. 13 Nov 2019.

Vancouver:

Izquierdo Verdiguier E. Kernel Feature Extraction Methods for Remote Sensing Data Analysis . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2014. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/10550/37288.

Council of Science Editors:

Izquierdo Verdiguier E. Kernel Feature Extraction Methods for Remote Sensing Data Analysis . [Doctoral Dissertation]. Universitat de Valencia; 2014. Available from: http://hdl.handle.net/10550/37288


University of Florida

10. Jiang, Nanzhi. Ground moving target detection and feature extraction with airborne phased array radar.

Degree: 2001, University of Florida

Subjects/Keywords: Calibration; Estimation methods; Feature extraction; Frequency ranges; Jamming; Phased arrays; Radar; Radar range; Supernova remnants; Vector autoregression

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APA (6th Edition):

Jiang, N. (2001). Ground moving target detection and feature extraction with airborne phased array radar. (Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/AA00024538

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):

Jiang, Nanzhi. “Ground moving target detection and feature extraction with airborne phased array radar.” 2001. Thesis, University of Florida. Accessed November 13, 2019. http://ufdc.ufl.edu/AA00024538.

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

MLA Handbook (7th Edition):

Jiang, Nanzhi. “Ground moving target detection and feature extraction with airborne phased array radar.” 2001. Web. 13 Nov 2019.

Vancouver:

Jiang N. Ground moving target detection and feature extraction with airborne phased array radar. [Internet] [Thesis]. University of Florida; 2001. [cited 2019 Nov 13]. Available from: http://ufdc.ufl.edu/AA00024538.

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

Council of Science Editors:

Jiang N. Ground moving target detection and feature extraction with airborne phased array radar. [Thesis]. University of Florida; 2001. Available from: http://ufdc.ufl.edu/AA00024538

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


Brno University of Technology

11. Válková, Hana. Detekce měkkých a tvrdých exudátů ve snímcích sítnice .

Degree: 2012, Brno University of Technology

 Tato práce se zabývá automatizovanou detekcí měkkých a tvrdých exudátů ve snímcích sítnice lidského oka. Práce v úvodu popisuje problematiku diabetu v souvislosti s poškozením… (more)

Subjects/Keywords: diabetická retinopatie; snímky očního pozadí; tvrdé exudáty; měkké exudáty; databáze DIARETDB1; adaptivní transformace kontrastu; metody segmentace lézí; prahování; výběr příznaků; klasifikace; diabetic retinopathy; fundus images; hard exudates; soft exudates; database DIARETDB1; adaptive transformation of contrast; methods for segmentation of lesions; thresholding; feature extraction; classification

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APA (6th Edition):

Válková, H. (2012). Detekce měkkých a tvrdých exudátů ve snímcích sítnice . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/12433

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):

Válková, Hana. “Detekce měkkých a tvrdých exudátů ve snímcích sítnice .” 2012. Thesis, Brno University of Technology. Accessed November 13, 2019. http://hdl.handle.net/11012/12433.

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

MLA Handbook (7th Edition):

Válková, Hana. “Detekce měkkých a tvrdých exudátů ve snímcích sítnice .” 2012. Web. 13 Nov 2019.

Vancouver:

Válková H. Detekce měkkých a tvrdých exudátů ve snímcích sítnice . [Internet] [Thesis]. Brno University of Technology; 2012. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/11012/12433.

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

Council of Science Editors:

Válková H. Detekce měkkých a tvrdých exudátů ve snímcích sítnice . [Thesis]. Brno University of Technology; 2012. Available from: http://hdl.handle.net/11012/12433

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


Brno University of Technology

12. Adamček, Ľubomír. Metody extrakce charakteristických rysů obličeje .

Degree: 2017, Brno University of Technology

 Tvár je dlhodobo jedna z najatraktívnejších dostupných biometrií človeka pre jej ľahké a pohodlné nasnímanie. Využitie je široké - od bezpečnosti, cez monitorovanie až po… (more)

Subjects/Keywords: biometria; rozpoznávanie tváre; metódy extrakcie; charakteristické rysy; vektor príznakov; PCA; analýza hlavných komponent; LBP; lokálne binárne vzory; HOG; histogramy orientovaných gradientov; biometry; face recognition; extraction methods; features; feature vector; PCA; principal component analysis; LBP; local binary patterns; HOG; histograms of oriented gradients

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APA (6th Edition):

Adamček, . (2017). Metody extrakce charakteristických rysů obličeje . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69806

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):

Adamček, Ľubomír. “Metody extrakce charakteristických rysů obličeje .” 2017. Thesis, Brno University of Technology. Accessed November 13, 2019. http://hdl.handle.net/11012/69806.

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

MLA Handbook (7th Edition):

Adamček, Ľubomír. “Metody extrakce charakteristických rysů obličeje .” 2017. Web. 13 Nov 2019.

Vancouver:

Adamček . Metody extrakce charakteristických rysů obličeje . [Internet] [Thesis]. Brno University of Technology; 2017. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/11012/69806.

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

Council of Science Editors:

Adamček . Metody extrakce charakteristických rysů obličeje . [Thesis]. Brno University of Technology; 2017. Available from: http://hdl.handle.net/11012/69806

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


Brno University of Technology

13. Dočekal, Martin. Pokročilé metody strojového učení pro klasifikaci textu .

Degree: 2017, Brno University of Technology

 Tato práce se zabývá pokročilými metodami strojového učení pro klasifikaci textu. Metody jsou nejprve popsány a poté je na základě těchto metod vytvořen systém sloužící… (more)

Subjects/Keywords: strojové učení; extrakce příznaků; Bag-of-words; TF-IDF; word2vec; doc2vec; hašování příznaků; k-nejbližších sousedů; Multinomiální naivní Bayes; Support Vector Machine; klasifikace; vyhodnocování klasifikátoru; předzpracování; slučování klasifikačních metod; vyvažovací algoritmy; machine-learning; feature extraction; Bag-of-words; TF-IDF; word2vec; doc2vec; feature hashing; k-nearest neighbors; Multinomial Naive Bayes; Support Vector Machine; classification; evaluation of classifier; preprocessing; ensemble classification methods; balancing algorithms

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

APA (6th Edition):

Dočekal, M. (2017). Pokročilé metody strojového učení pro klasifikaci textu . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/69700

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):

Dočekal, Martin. “Pokročilé metody strojového učení pro klasifikaci textu .” 2017. Thesis, Brno University of Technology. Accessed November 13, 2019. http://hdl.handle.net/11012/69700.

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

MLA Handbook (7th Edition):

Dočekal, Martin. “Pokročilé metody strojového učení pro klasifikaci textu .” 2017. Web. 13 Nov 2019.

Vancouver:

Dočekal M. Pokročilé metody strojového učení pro klasifikaci textu . [Internet] [Thesis]. Brno University of Technology; 2017. [cited 2019 Nov 13]. Available from: http://hdl.handle.net/11012/69700.

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

Council of Science Editors:

Dočekal M. Pokročilé metody strojového učení pro klasifikaci textu . [Thesis]. Brno University of Technology; 2017. Available from: http://hdl.handle.net/11012/69700

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

14. Laaksonen, Jorma. Subspace Classifiers in Recognition of Handwritten Digits.

Degree: 2001, Helsinki University of Technology

This thesis consists of two parts. The first part reviews the general structure of a pattern recognition system and, in particular, various statistical and neural… (more)

Subjects/Keywords: pattern recognition; adaptive systems; neural networks; statistical classification; subspace methods; prototype-based classification; feature extraction; optical character recognition; handwritten digits; classifier comparison; benchmarking study

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

APA (6th Edition):

Laaksonen, J. (2001). Subspace Classifiers in Recognition of Handwritten Digits. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/199X/isbn9512254794/

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):

Laaksonen, Jorma. “Subspace Classifiers in Recognition of Handwritten Digits.” 2001. Thesis, Helsinki University of Technology. Accessed November 13, 2019. http://lib.tkk.fi/Diss/199X/isbn9512254794/.

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

MLA Handbook (7th Edition):

Laaksonen, Jorma. “Subspace Classifiers in Recognition of Handwritten Digits.” 2001. Web. 13 Nov 2019.

Vancouver:

Laaksonen J. Subspace Classifiers in Recognition of Handwritten Digits. [Internet] [Thesis]. Helsinki University of Technology; 2001. [cited 2019 Nov 13]. Available from: http://lib.tkk.fi/Diss/199X/isbn9512254794/.

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

Council of Science Editors:

Laaksonen J. Subspace Classifiers in Recognition of Handwritten Digits. [Thesis]. Helsinki University of Technology; 2001. Available from: http://lib.tkk.fi/Diss/199X/isbn9512254794/

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

.