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You searched for +publisher:"University of Texas – Austin" +contributor:("Crawford, Melba M."). Showing records 1 – 5 of 5 total matches.

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1. Chen, Yang-Chi, 1973-. Knowledge-based learning for classification of hyperspectral data.

Degree: Operations Research and Industrial Engineering, 2007, University of Texas – Austin

Subjects/Keywords: Hyperspectral data; Multispectral photography; Remote sensing

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

APA (6th Edition):

Chen, Yang-Chi, 1. (2007). Knowledge-based learning for classification of hyperspectral data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/15971

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

Chicago Manual of Style (16th Edition):

Chen, Yang-Chi, 1973-. “Knowledge-based learning for classification of hyperspectral data.” 2007. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/15971.

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

MLA Handbook (7th Edition):

Chen, Yang-Chi, 1973-. “Knowledge-based learning for classification of hyperspectral data.” 2007. Web. 18 Mar 2019.

Vancouver:

Chen, Yang-Chi 1. Knowledge-based learning for classification of hyperspectral data. [Internet] [Thesis]. University of Texas – Austin; 2007. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/15971.

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

Council of Science Editors:

Chen, Yang-Chi 1. Knowledge-based learning for classification of hyperspectral data. [Thesis]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/15971

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


University of Texas – Austin

2. Jun, Goo. Transfer learning for classification of spatially varying data.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

 Many real-world datasets have spatial components that provide valuable information about characteristics of the data. In this dissertation, a novel framework for adaptive models that… (more)

Subjects/Keywords: Machine learning; Classification and semi-supervised learning algorithms; Gaussian processes; Gaussian process regressions; Spatial statistics

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

APA (6th Edition):

Jun, G. (2010). Transfer learning for classification of spatially varying data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-1962

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, Goo. “Transfer learning for classification of spatially varying data.” 2010. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-1962.

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

MLA Handbook (7th Edition):

Jun, Goo. “Transfer learning for classification of spatially varying data.” 2010. Web. 18 Mar 2019.

Vancouver:

Jun G. Transfer learning for classification of spatially varying data. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1962.

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

Council of Science Editors:

Jun G. Transfer learning for classification of spatially varying data. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1962

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


University of Texas – Austin

3. Morgan, Joseph Troy. Adaptive hierarchical classification with limited training data.

Degree: Mechanical Engineering, 2002, University of Texas – Austin

 This research focused on the development of a hierarchical approach for classification that is robust with respect to training data that are limited both in… (more)

Subjects/Keywords: Pattern recognition systems; Statistical decision

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

APA (6th Edition):

Morgan, J. T. (2002). Adaptive hierarchical classification with limited training data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/802

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):

Morgan, Joseph Troy. “Adaptive hierarchical classification with limited training data.” 2002. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/802.

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

MLA Handbook (7th Edition):

Morgan, Joseph Troy. “Adaptive hierarchical classification with limited training data.” 2002. Web. 18 Mar 2019.

Vancouver:

Morgan JT. Adaptive hierarchical classification with limited training data. [Internet] [Thesis]. University of Texas – Austin; 2002. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/802.

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

Council of Science Editors:

Morgan JT. Adaptive hierarchical classification with limited training data. [Thesis]. University of Texas – Austin; 2002. Available from: http://hdl.handle.net/2152/802

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


University of Texas – Austin

4. Korycinski, Donna Kay. Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems.

Degree: Mechanical Engineering, 2003, University of Texas – Austin

Subjects/Keywords: Pattern perception; Pattern recognition systems; Mathematical optimization

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

APA (6th Edition):

Korycinski, D. K. (2003). Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/711

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):

Korycinski, Donna Kay. “Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems.” 2003. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/711.

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

MLA Handbook (7th Edition):

Korycinski, Donna Kay. “Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems.” 2003. Web. 18 Mar 2019.

Vancouver:

Korycinski DK. Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems. [Internet] [Thesis]. University of Texas – Austin; 2003. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/711.

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

Council of Science Editors:

Korycinski DK. Investigating the use of tabu search to find near-optimal solutions in multiclassifier systems. [Thesis]. University of Texas – Austin; 2003. Available from: http://hdl.handle.net/2152/711

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


University of Texas – Austin

5. Slatton, Kenneth Clinton. Adaptive multiscale estimation for fusing image data.

Degree: Electrical and Computer Engineering, 2001, University of Texas – Austin

Subjects/Keywords: Remote sensing – Mathematical models; Synthetic aperture radar; Altimeter – Data processing

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

APA (6th Edition):

Slatton, K. C. (2001). Adaptive multiscale estimation for fusing image data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1661

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):

Slatton, Kenneth Clinton. “Adaptive multiscale estimation for fusing image data.” 2001. Thesis, University of Texas – Austin. Accessed March 18, 2019. http://hdl.handle.net/2152/1661.

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

MLA Handbook (7th Edition):

Slatton, Kenneth Clinton. “Adaptive multiscale estimation for fusing image data.” 2001. Web. 18 Mar 2019.

Vancouver:

Slatton KC. Adaptive multiscale estimation for fusing image data. [Internet] [Thesis]. University of Texas – Austin; 2001. [cited 2019 Mar 18]. Available from: http://hdl.handle.net/2152/1661.

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

Council of Science Editors:

Slatton KC. Adaptive multiscale estimation for fusing image data. [Thesis]. University of Texas – Austin; 2001. Available from: http://hdl.handle.net/2152/1661

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

.