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You searched for subject:(Data Classification). Showing records 1 – 30 of 2846 total matches.

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Central Connecticut State University

1. Ironside, Brian Michael, 1978-. Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers.

Degree: Department of Mathematical Sciences, 2016, Central Connecticut State University

Two approaches are tested to improve the predictive performance of classification ensembles, individually, and in combination. Eight datasets from the UCI machine learning repository are… (more)

Subjects/Keywords: Data mining.; Classification.

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

APA (6th Edition):

Ironside, Brian Michael, 1. (2016). Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers. (Thesis). Central Connecticut State University. Retrieved from http://content.library.ccsu.edu/u?/ccsutheses,2357

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):

Ironside, Brian Michael, 1978-. “Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers.” 2016. Thesis, Central Connecticut State University. Accessed September 28, 2020. http://content.library.ccsu.edu/u?/ccsutheses,2357.

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

MLA Handbook (7th Edition):

Ironside, Brian Michael, 1978-. “Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers.” 2016. Web. 28 Sep 2020.

Vancouver:

Ironside, Brian Michael 1. Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers. [Internet] [Thesis]. Central Connecticut State University; 2016. [cited 2020 Sep 28]. Available from: http://content.library.ccsu.edu/u?/ccsutheses,2357.

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

Council of Science Editors:

Ironside, Brian Michael 1. Improving the Performance of Ensemble Classifier Models through the Local Specialization of Base Classifiers. [Thesis]. Central Connecticut State University; 2016. Available from: http://content.library.ccsu.edu/u?/ccsutheses,2357

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


University of Houston

2. Almogahed, Bassam A. 1980-. Toward Improved Classification of Imbalanced Data.

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

 There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced.… (more)

Subjects/Keywords: Classification; Imbalanced data

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APA (6th Edition):

Almogahed, B. A. 1. (2014). Toward Improved Classification of Imbalanced Data. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1910

Chicago Manual of Style (16th Edition):

Almogahed, Bassam A 1980-. “Toward Improved Classification of Imbalanced Data.” 2014. Doctoral Dissertation, University of Houston. Accessed September 28, 2020. http://hdl.handle.net/10657/1910.

MLA Handbook (7th Edition):

Almogahed, Bassam A 1980-. “Toward Improved Classification of Imbalanced Data.” 2014. Web. 28 Sep 2020.

Vancouver:

Almogahed BA1. Toward Improved Classification of Imbalanced Data. [Internet] [Doctoral dissertation]. University of Houston; 2014. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10657/1910.

Council of Science Editors:

Almogahed BA1. Toward Improved Classification of Imbalanced Data. [Doctoral Dissertation]. University of Houston; 2014. Available from: http://hdl.handle.net/10657/1910


Anna University

3. Srinivasan V. Prediction of investment in share Market using fuzzy fast Classification;.

Degree: Prediction of investment in share Market using fuzzy fast Classification, 2015, Anna University

Data mining has gained more attention in the information industry newlinedue to the wide availability of enormous amount of data and the need for newlinetuning… (more)

Subjects/Keywords: Data mining; Fuzzy fast Classification

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APA (6th Edition):

V, S. (2015). Prediction of investment in share Market using fuzzy fast Classification;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/33599

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):

V, Srinivasan. “Prediction of investment in share Market using fuzzy fast Classification;.” 2015. Thesis, Anna University. Accessed September 28, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/33599.

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

MLA Handbook (7th Edition):

V, Srinivasan. “Prediction of investment in share Market using fuzzy fast Classification;.” 2015. Web. 28 Sep 2020.

Vancouver:

V S. Prediction of investment in share Market using fuzzy fast Classification;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Sep 28]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/33599.

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

Council of Science Editors:

V S. Prediction of investment in share Market using fuzzy fast Classification;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/33599

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


University of Pretoria

4. Van der Walt, Christiaan Maarten. Data measures that characterise classification problems.

Degree: Electrical, Electronic and Computer Engineering, 2008, University of Pretoria

 We have a wide-range of classifiers today that are employed in numerous applications, from credit scoring to speech-processing, with great technical and commercial success. No… (more)

Subjects/Keywords: Classifier selection; Data measures; Data characteristics; Artificial data; Data analysis; Classification; Supervised learning; Pattern recognition; Meta-classification; Classification prediction; UCTD

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

APA (6th Edition):

Van der Walt, C. (2008). Data measures that characterise classification problems. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/27624

Chicago Manual of Style (16th Edition):

Van der Walt, Christiaan. “Data measures that characterise classification problems.” 2008. Masters Thesis, University of Pretoria. Accessed September 28, 2020. http://hdl.handle.net/2263/27624.

MLA Handbook (7th Edition):

Van der Walt, Christiaan. “Data measures that characterise classification problems.” 2008. Web. 28 Sep 2020.

Vancouver:

Van der Walt C. Data measures that characterise classification problems. [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2263/27624.

Council of Science Editors:

Van der Walt C. Data measures that characterise classification problems. [Masters Thesis]. University of Pretoria; 2008. Available from: http://hdl.handle.net/2263/27624


University of Pretoria

5. [No author]. Data measures that characterise classification problems .

Degree: 2008, University of Pretoria

 We have a wide-range of classifiers today that are employed in numerous applications, from credit scoring to speech-processing, with great technical and commercial success. No… (more)

Subjects/Keywords: Classifier selection; Data measures; Data characteristics; Artificial data; Data analysis; Classification; Supervised learning; Pattern recognition; Meta-classification; Classification prediction; UCTD

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

APA (6th Edition):

author], [. (2008). Data measures that characterise classification problems . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-08292008-162648/

Chicago Manual of Style (16th Edition):

author], [No. “Data measures that characterise classification problems .” 2008. Masters Thesis, University of Pretoria. Accessed September 28, 2020. http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

MLA Handbook (7th Edition):

author], [No. “Data measures that characterise classification problems .” 2008. Web. 28 Sep 2020.

Vancouver:

author] [. Data measures that characterise classification problems . [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Sep 28]. Available from: http://upetd.up.ac.za/thesis/available/etd-08292008-162648/.

Council of Science Editors:

author] [. Data measures that characterise classification problems . [Masters Thesis]. University of Pretoria; 2008. Available from: http://upetd.up.ac.za/thesis/available/etd-08292008-162648/

6. Bouillot, Flavien. Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset.

Degree: Docteur es, Informatique, 2015, Montpellier

Au quotidien, le réflexe de classifier est omniprésent et inconscient. Par exemple dans le processus de prise de décision où face à un élément (un… (more)

Subjects/Keywords: Classification textuelle; Petits volume de données; Méta-Classification; Text classification; Few data classification; Meta-Classification

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

APA (6th Edition):

Bouillot, F. (2015). Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset. (Doctoral Dissertation). Montpellier. Retrieved from http://www.theses.fr/2015MONTS167

Chicago Manual of Style (16th Edition):

Bouillot, Flavien. “Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset.” 2015. Doctoral Dissertation, Montpellier. Accessed September 28, 2020. http://www.theses.fr/2015MONTS167.

MLA Handbook (7th Edition):

Bouillot, Flavien. “Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset.” 2015. Web. 28 Sep 2020.

Vancouver:

Bouillot F. Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset. [Internet] [Doctoral dissertation]. Montpellier; 2015. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2015MONTS167.

Council of Science Editors:

Bouillot F. Classification de textes : de nouvelles pondérations adaptées aux petits volumes : Text Classification : new weights suitable for small dataset. [Doctoral Dissertation]. Montpellier; 2015. Available from: http://www.theses.fr/2015MONTS167


Virginia Tech

7. King, Michael Allen. Ensemble Learning Techniques for Structured and Unstructured Data.

Degree: PhD, Business Information Technology, 2015, Virginia Tech

 This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual… (more)

Subjects/Keywords: ensemble methods; data mining; machine learning; classification; structured data; unstructured data

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APA (6th Edition):

King, M. A. (2015). Ensemble Learning Techniques for Structured and Unstructured Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51667

Chicago Manual of Style (16th Edition):

King, Michael Allen. “Ensemble Learning Techniques for Structured and Unstructured Data.” 2015. Doctoral Dissertation, Virginia Tech. Accessed September 28, 2020. http://hdl.handle.net/10919/51667.

MLA Handbook (7th Edition):

King, Michael Allen. “Ensemble Learning Techniques for Structured and Unstructured Data.” 2015. Web. 28 Sep 2020.

Vancouver:

King MA. Ensemble Learning Techniques for Structured and Unstructured Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10919/51667.

Council of Science Editors:

King MA. Ensemble Learning Techniques for Structured and Unstructured Data. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/51667


University of Arizona

8. Washburn, Ammon. High-Confidence Learning from Uncertain Data with High Dimensionality .

Degree: 2018, University of Arizona

 Some of the most challenging issues in big data are size, scalability and reliability. Big data, such as pictures, videos, and text, have innate structure… (more)

Subjects/Keywords: data classification; data uncertainty; high dimensional data; machine learning; optimization

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APA (6th Edition):

Washburn, A. (2018). High-Confidence Learning from Uncertain Data with High Dimensionality . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/631476

Chicago Manual of Style (16th Edition):

Washburn, Ammon. “High-Confidence Learning from Uncertain Data with High Dimensionality .” 2018. Doctoral Dissertation, University of Arizona. Accessed September 28, 2020. http://hdl.handle.net/10150/631476.

MLA Handbook (7th Edition):

Washburn, Ammon. “High-Confidence Learning from Uncertain Data with High Dimensionality .” 2018. Web. 28 Sep 2020.

Vancouver:

Washburn A. High-Confidence Learning from Uncertain Data with High Dimensionality . [Internet] [Doctoral dissertation]. University of Arizona; 2018. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10150/631476.

Council of Science Editors:

Washburn A. High-Confidence Learning from Uncertain Data with High Dimensionality . [Doctoral Dissertation]. University of Arizona; 2018. Available from: http://hdl.handle.net/10150/631476


Texas A&M University

9. Kollegala, Revathi. The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description.

Degree: MS, Electrical Engineering, 2012, Texas A&M University

 Detection of targets in hyperspectral images is a specific case of one-class classification. It is particularly relevant in the area of remote sensing and has… (more)

Subjects/Keywords: hyperspectral; classification; support; vector; description; domain; data

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

APA (6th Edition):

Kollegala, R. (2012). The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11148

Chicago Manual of Style (16th Edition):

Kollegala, Revathi. “The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description.” 2012. Masters Thesis, Texas A&M University. Accessed September 28, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11148.

MLA Handbook (7th Edition):

Kollegala, Revathi. “The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description.” 2012. Web. 28 Sep 2020.

Vancouver:

Kollegala R. The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description. [Internet] [Masters thesis]. Texas A&M University; 2012. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11148.

Council of Science Editors:

Kollegala R. The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description. [Masters Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11148


NSYSU

10. Yang, Cheng-Ju. Image classification via successive core tensor selection procedure.

Degree: Master, Applied Mathematics, 2018, NSYSU

 In the field of artificial intelligence, high-order tensor data have been studied and analyzed, such as the automated optical inspection and MRI. Therefore, tensor decompositions… (more)

Subjects/Keywords: data feature extraction; image classification; tensor decomposition

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APA (6th Edition):

Yang, C. (2018). Image classification via successive core tensor selection procedure. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922

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):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.

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

MLA Handbook (7th Edition):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Web. 28 Sep 2020.

Vancouver:

Yang C. Image classification via successive core tensor selection procedure. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.

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

Council of Science Editors:

Yang C. Image classification via successive core tensor selection procedure. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922

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

11. Martins, Hélder José Pedrosa de Abreu. Deteção de convulsões epiléticas a partir de eletroencefalogramas.

Degree: 2016, Instituto Politécnico do Porto

A epilepsia atinge cerca de 1% da população mundial, e é caracterizada pela ocorrência de crises espontâneas. Pretende-se detetar (e prever) convulsões analisando os dados… (more)

Subjects/Keywords: Classificação; Data mining; Motifs; Classification; Sistemas Computacionais

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APA (6th Edition):

Martins, H. J. P. d. A. (2016). Deteção de convulsões epiléticas a partir de eletroencefalogramas. (Thesis). Instituto Politécnico do Porto. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/10979

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):

Martins, Hélder José Pedrosa de Abreu. “Deteção de convulsões epiléticas a partir de eletroencefalogramas.” 2016. Thesis, Instituto Politécnico do Porto. Accessed September 28, 2020. https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/10979.

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

MLA Handbook (7th Edition):

Martins, Hélder José Pedrosa de Abreu. “Deteção de convulsões epiléticas a partir de eletroencefalogramas.” 2016. Web. 28 Sep 2020.

Vancouver:

Martins HJPdA. Deteção de convulsões epiléticas a partir de eletroencefalogramas. [Internet] [Thesis]. Instituto Politécnico do Porto; 2016. [cited 2020 Sep 28]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/10979.

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

Council of Science Editors:

Martins HJPdA. Deteção de convulsões epiléticas a partir de eletroencefalogramas. [Thesis]. Instituto Politécnico do Porto; 2016. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/10979

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


NSYSU

12. Chen, Ming-cheng. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.

Degree: Master, Computer Science and Engineering, 2008, NSYSU

 The microarray technology plays an important role of clinical oncology field. The patient can be diagnosed a symptom about cancer through microarray data. Currently, to… (more)

Subjects/Keywords: Microarray Data Classification; Genetic Algorithm; Fuzzy Theory

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

APA (6th Edition):

Chen, M. (2008). A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834

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, Ming-cheng. “A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.” 2008. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834.

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

MLA Handbook (7th Edition):

Chen, Ming-cheng. “A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.” 2008. Web. 28 Sep 2020.

Vancouver:

Chen M. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. [Internet] [Thesis]. NSYSU; 2008. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834.

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

Council of Science Editors:

Chen M. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834

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


NSYSU

13. Chou, Chun-han. Malware Detection System Based on API Log Data Mining.

Degree: Master, Computer Science and Engineering, 2013, NSYSU

 As information technology improves, the Internet is involved in every area in our daily life. When the mobile devices and cloud computing technology start to… (more)

Subjects/Keywords: Classification; System Call; API; Data Mining; Malware

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

APA (6th Edition):

Chou, C. (2013). Malware Detection System Based on API Log Data Mining. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717

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):

Chou, Chun-han. “Malware Detection System Based on API Log Data Mining.” 2013. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717.

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

MLA Handbook (7th Edition):

Chou, Chun-han. “Malware Detection System Based on API Log Data Mining.” 2013. Web. 28 Sep 2020.

Vancouver:

Chou C. Malware Detection System Based on API Log Data Mining. [Internet] [Thesis]. NSYSU; 2013. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717.

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

Council of Science Editors:

Chou C. Malware Detection System Based on API Log Data Mining. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0714113-160717

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


NSYSU

14. Huang, Bor-Chen. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.

Degree: Master, Computer Science and Engineering, 2015, NSYSU

 As the social network gains more popular, people would like to find the related information from social nets to figure out some signs before or… (more)

Subjects/Keywords: Negative emotion detection; Data mining; Emotion classification

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APA (6th Edition):

Huang, B. (2015). Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224

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, Bor-Chen. “Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.” 2015. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224.

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

MLA Handbook (7th Edition):

Huang, Bor-Chen. “Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications.” 2015. Web. 28 Sep 2020.

Vancouver:

Huang B. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224.

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

Council of Science Editors:

Huang B. Negative Emotion Detection with Consideration of Events and Places for Chinese Posts on Facebook and Applications. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130115-184224

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


Universiteit Utrecht

15. Lentink, M.D. Automatic classification of orders lines in joint Dealer Management Systems.

Degree: 2016, Universiteit Utrecht

 In this thesis we looked at different data originating from several Dealer Management Systems. By comparing the different data we tried to find a field… (more)

Subjects/Keywords: classification; rdc; dealer management system; data mining

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APA (6th Edition):

Lentink, M. D. (2016). Automatic classification of orders lines in joint Dealer Management Systems. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/338671

Chicago Manual of Style (16th Edition):

Lentink, M D. “Automatic classification of orders lines in joint Dealer Management Systems.” 2016. Masters Thesis, Universiteit Utrecht. Accessed September 28, 2020. http://dspace.library.uu.nl:8080/handle/1874/338671.

MLA Handbook (7th Edition):

Lentink, M D. “Automatic classification of orders lines in joint Dealer Management Systems.” 2016. Web. 28 Sep 2020.

Vancouver:

Lentink MD. Automatic classification of orders lines in joint Dealer Management Systems. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2020 Sep 28]. Available from: http://dspace.library.uu.nl:8080/handle/1874/338671.

Council of Science Editors:

Lentink MD. Automatic classification of orders lines in joint Dealer Management Systems. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/338671


University of Illinois – Chicago

16. Li, Huayi. Detecting Opinion Spam in Commercial Review Websites.

Degree: 2016, University of Illinois – Chicago

 Review websites have become very important platforms for consumers to compare and evaluate products or services. However, review systems are often targeted by opinion spam.… (more)

Subjects/Keywords: Opinion Spam; Fake Reviews; Classification; Data Mining

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APA (6th Edition):

Li, H. (2016). Detecting Opinion Spam in Commercial Review Websites. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/20915

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):

Li, Huayi. “Detecting Opinion Spam in Commercial Review Websites.” 2016. Thesis, University of Illinois – Chicago. Accessed September 28, 2020. http://hdl.handle.net/10027/20915.

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

MLA Handbook (7th Edition):

Li, Huayi. “Detecting Opinion Spam in Commercial Review Websites.” 2016. Web. 28 Sep 2020.

Vancouver:

Li H. Detecting Opinion Spam in Commercial Review Websites. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10027/20915.

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

Council of Science Editors:

Li H. Detecting Opinion Spam in Commercial Review Websites. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/20915

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


University of Toronto

17. Gao, Fan. Novel Classification Method Development for Microbiome Data.

Degree: 2019, University of Toronto

The microbiome data is a popular research topic used in recent medical science. However, the microbiome data has its own features, such as high skewness,… (more)

Subjects/Keywords: Classification methods; diagnostic test; Microbiome Data; 0308

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APA (6th Edition):

Gao, F. (2019). Novel Classification Method Development for Microbiome Data. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/98324

Chicago Manual of Style (16th Edition):

Gao, Fan. “Novel Classification Method Development for Microbiome Data.” 2019. Masters Thesis, University of Toronto. Accessed September 28, 2020. http://hdl.handle.net/1807/98324.

MLA Handbook (7th Edition):

Gao, Fan. “Novel Classification Method Development for Microbiome Data.” 2019. Web. 28 Sep 2020.

Vancouver:

Gao F. Novel Classification Method Development for Microbiome Data. [Internet] [Masters thesis]. University of Toronto; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1807/98324.

Council of Science Editors:

Gao F. Novel Classification Method Development for Microbiome Data. [Masters Thesis]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/98324


George Mason University

18. Chen, Tianwen. Random Subspace Method in Classification and Mapping of fMRI Data Patterns .

Degree: 2011, George Mason University

 The functional magnetic resonance imaging (fMRI) technique is widely used in studying human brain functions. It measures brain activities both spatially and temporally. The past… (more)

Subjects/Keywords: Random Subspace; FMRI Data; Classification; Pattern Mapping

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APA (6th Edition):

Chen, T. (2011). Random Subspace Method in Classification and Mapping of fMRI Data Patterns . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/6393

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, Tianwen. “Random Subspace Method in Classification and Mapping of fMRI Data Patterns .” 2011. Thesis, George Mason University. Accessed September 28, 2020. http://hdl.handle.net/1920/6393.

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

MLA Handbook (7th Edition):

Chen, Tianwen. “Random Subspace Method in Classification and Mapping of fMRI Data Patterns .” 2011. Web. 28 Sep 2020.

Vancouver:

Chen T. Random Subspace Method in Classification and Mapping of fMRI Data Patterns . [Internet] [Thesis]. George Mason University; 2011. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1920/6393.

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

Council of Science Editors:

Chen T. Random Subspace Method in Classification and Mapping of fMRI Data Patterns . [Thesis]. George Mason University; 2011. Available from: http://hdl.handle.net/1920/6393

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


Delft University of Technology

19. van Dongen, Kirsten (author). Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.

Degree: 2019, Delft University of Technology

 Trees are an important aspect of the world around us, and play a sufficient role in our daily lives. They contribute to human health and… (more)

Subjects/Keywords: AHN; tree classification; laser point data

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APA (6th Edition):

van Dongen, K. (. (2019). Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815

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):

van Dongen, Kirsten (author). “Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.” 2019. Thesis, Delft University of Technology. Accessed September 28, 2020. http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815.

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

MLA Handbook (7th Edition):

van Dongen, Kirsten (author). “Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis.” 2019. Web. 28 Sep 2020.

Vancouver:

van Dongen K(. Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. [Internet] [Thesis]. Delft University of Technology; 2019. [cited 2020 Sep 28]. Available from: http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815.

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

Council of Science Editors:

van Dongen K(. Random Forest Classification of three different species of trees in Delft, based on AHN point clouds: Additional Thesis. [Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:70b0b406-b247-4212-8e66-02534935b815

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


University of Ghana

20. Kuditchar, B. Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems .

Degree: 2019, University of Ghana

 Class imbalance problem is prevalent in many real-world domains and as such has become an area of increasing interest for many researchers. In binary classification(more)

Subjects/Keywords: Class Imbalance; Data Sampling; Binary Classification

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

APA (6th Edition):

Kuditchar, B. (2019). Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems . (Masters Thesis). University of Ghana. Retrieved from http://ugspace.ug.edu.gh/handle/123456789/35240

Chicago Manual of Style (16th Edition):

Kuditchar, B. “Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems .” 2019. Masters Thesis, University of Ghana. Accessed September 28, 2020. http://ugspace.ug.edu.gh/handle/123456789/35240.

MLA Handbook (7th Edition):

Kuditchar, B. “Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems .” 2019. Web. 28 Sep 2020.

Vancouver:

Kuditchar B. Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems . [Internet] [Masters thesis]. University of Ghana; 2019. [cited 2020 Sep 28]. Available from: http://ugspace.ug.edu.gh/handle/123456789/35240.

Council of Science Editors:

Kuditchar B. Hybrid Cluster-Based Sampling Technique for Class Imbalance Problems . [Masters Thesis]. University of Ghana; 2019. Available from: http://ugspace.ug.edu.gh/handle/123456789/35240


Kansas State University

21. Venkatramolla, Sudesh Kumar. Machine learning and data science for a household-specific poverty level prediction task.

Degree: MS, Department of Computer Science, 2019, Kansas State University

 This project focuses on a prediction task from the Kaggle data science challenge site: prediction of the poverty level of individual households using supervised classification(more)

Subjects/Keywords: Machine Learning; Data Science; Prediction; Classification

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APA (6th Edition):

Venkatramolla, S. K. (2019). Machine learning and data science for a household-specific poverty level prediction task. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/39520

Chicago Manual of Style (16th Edition):

Venkatramolla, Sudesh Kumar. “Machine learning and data science for a household-specific poverty level prediction task.” 2019. Masters Thesis, Kansas State University. Accessed September 28, 2020. http://hdl.handle.net/2097/39520.

MLA Handbook (7th Edition):

Venkatramolla, Sudesh Kumar. “Machine learning and data science for a household-specific poverty level prediction task.” 2019. Web. 28 Sep 2020.

Vancouver:

Venkatramolla SK. Machine learning and data science for a household-specific poverty level prediction task. [Internet] [Masters thesis]. Kansas State University; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2097/39520.

Council of Science Editors:

Venkatramolla SK. Machine learning and data science for a household-specific poverty level prediction task. [Masters Thesis]. Kansas State University; 2019. Available from: http://hdl.handle.net/2097/39520


Louisiana State University

22. Sathiaraj, David. On Identifying Critical Nuggets Of Information During Classification Task.

Degree: PhD, Computer Sciences, 2013, Louisiana State University

 In large databases, there may exist critical nuggets - small collections of records or instances that contain domain-specific important information. This information can be used… (more)

Subjects/Keywords: critical nuggets; big data analytics; classification accuracy; classification algorithms; data mining; misclassification costs; machine learning

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APA (6th Edition):

Sathiaraj, D. (2013). On Identifying Critical Nuggets Of Information During Classification Task. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04102013-120012 ; https://digitalcommons.lsu.edu/gradschool_dissertations/506

Chicago Manual of Style (16th Edition):

Sathiaraj, David. “On Identifying Critical Nuggets Of Information During Classification Task.” 2013. Doctoral Dissertation, Louisiana State University. Accessed September 28, 2020. etd-04102013-120012 ; https://digitalcommons.lsu.edu/gradschool_dissertations/506.

MLA Handbook (7th Edition):

Sathiaraj, David. “On Identifying Critical Nuggets Of Information During Classification Task.” 2013. Web. 28 Sep 2020.

Vancouver:

Sathiaraj D. On Identifying Critical Nuggets Of Information During Classification Task. [Internet] [Doctoral dissertation]. Louisiana State University; 2013. [cited 2020 Sep 28]. Available from: etd-04102013-120012 ; https://digitalcommons.lsu.edu/gradschool_dissertations/506.

Council of Science Editors:

Sathiaraj D. On Identifying Critical Nuggets Of Information During Classification Task. [Doctoral Dissertation]. Louisiana State University; 2013. Available from: etd-04102013-120012 ; https://digitalcommons.lsu.edu/gradschool_dissertations/506


Addis Ababa University

23. TADESSE, BEYENE. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .

Degree: 2012, Addis Ababa University

Data mining is a relatively new field whose major objective is to extract knowledge hidden in large amounts of data. Vital statistics data offer a… (more)

Subjects/Keywords: vital statistics data; Machine Learning; data mining; predictive models; classification; Weka.

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APA (6th Edition):

TADESSE, B. (2012). MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/dspace/handle/123456789/2761

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):

TADESSE, BEYENE. “MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .” 2012. Thesis, Addis Ababa University. Accessed September 28, 2020. http://etd.aau.edu.et/dspace/handle/123456789/2761.

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

MLA Handbook (7th Edition):

TADESSE, BEYENE. “MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .” 2012. Web. 28 Sep 2020.

Vancouver:

TADESSE B. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . [Internet] [Thesis]. Addis Ababa University; 2012. [cited 2020 Sep 28]. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2761.

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

Council of Science Editors:

TADESSE B. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . [Thesis]. Addis Ababa University; 2012. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2761

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

24. ALMEIDA, Marcos Antonio Martins de. Statistical analysis applied to data classification and image filtering .

Degree: 2016, Universidade Federal de Pernambuco

 Statistical analysis is a tool of wide applicability in several areas of scientific knowledge. This thesis makes use of statistical analysis in two different applications:… (more)

Subjects/Keywords: Electrical Eingineering; Data processing; Data classification; Image filtering

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APA (6th Edition):

ALMEIDA, M. A. M. d. (2016). Statistical analysis applied to data classification and image filtering . (Doctoral Dissertation). Universidade Federal de Pernambuco. Retrieved from https://repositorio.ufpe.br/handle/123456789/25506

Chicago Manual of Style (16th Edition):

ALMEIDA, Marcos Antonio Martins de. “Statistical analysis applied to data classification and image filtering .” 2016. Doctoral Dissertation, Universidade Federal de Pernambuco. Accessed September 28, 2020. https://repositorio.ufpe.br/handle/123456789/25506.

MLA Handbook (7th Edition):

ALMEIDA, Marcos Antonio Martins de. “Statistical analysis applied to data classification and image filtering .” 2016. Web. 28 Sep 2020.

Vancouver:

ALMEIDA MAMd. Statistical analysis applied to data classification and image filtering . [Internet] [Doctoral dissertation]. Universidade Federal de Pernambuco; 2016. [cited 2020 Sep 28]. Available from: https://repositorio.ufpe.br/handle/123456789/25506.

Council of Science Editors:

ALMEIDA MAMd. Statistical analysis applied to data classification and image filtering . [Doctoral Dissertation]. Universidade Federal de Pernambuco; 2016. Available from: https://repositorio.ufpe.br/handle/123456789/25506


Youngstown State University

25. Sajja, Sunitha. Data Mining of Medical Datasets with Missing Attributes from Different Sources.

Degree: MSin Mathematics, Department of Mathematics and Statistics, 2010, Youngstown State University

 Two major problems in data mining are 1) dealing with missing values in the datasets used for knowledge discovery, and 2) using one data set… (more)

Subjects/Keywords: Computer Science; data mining; missing attributes; data classification; outliers

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APA (6th Edition):

Sajja, S. (2010). Data Mining of Medical Datasets with Missing Attributes from Different Sources. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu1300298263

Chicago Manual of Style (16th Edition):

Sajja, Sunitha. “Data Mining of Medical Datasets with Missing Attributes from Different Sources.” 2010. Masters Thesis, Youngstown State University. Accessed September 28, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1300298263.

MLA Handbook (7th Edition):

Sajja, Sunitha. “Data Mining of Medical Datasets with Missing Attributes from Different Sources.” 2010. Web. 28 Sep 2020.

Vancouver:

Sajja S. Data Mining of Medical Datasets with Missing Attributes from Different Sources. [Internet] [Masters thesis]. Youngstown State University; 2010. [cited 2020 Sep 28]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1300298263.

Council of Science Editors:

Sajja S. Data Mining of Medical Datasets with Missing Attributes from Different Sources. [Masters Thesis]. Youngstown State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1300298263


Universitat Politècnica de València

26. Esteve García, Albert. Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors .

Degree: 2017, Universitat Politècnica de València

 Most of the data referenced by sequential and parallel applications running in current chip multiprocessors are referenced by a single thread, i.e., private. Recent proposals… (more)

Subjects/Keywords: Data classification; Cache coherence; TLB; Private-shared; Read-only data

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APA (6th Edition):

Esteve García, A. (2017). Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors . (Doctoral Dissertation). Universitat Politècnica de València. Retrieved from http://hdl.handle.net/10251/86136

Chicago Manual of Style (16th Edition):

Esteve García, Albert. “Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors .” 2017. Doctoral Dissertation, Universitat Politècnica de València. Accessed September 28, 2020. http://hdl.handle.net/10251/86136.

MLA Handbook (7th Edition):

Esteve García, Albert. “Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors .” 2017. Web. 28 Sep 2020.

Vancouver:

Esteve García A. Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors . [Internet] [Doctoral dissertation]. Universitat Politècnica de València; 2017. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10251/86136.

Council of Science Editors:

Esteve García A. Design of Efficient TLB-based Data Classification Mechanisms in Chip Multiprocessors . [Doctoral Dissertation]. Universitat Politècnica de València; 2017. Available from: http://hdl.handle.net/10251/86136


University of Guelph

27. D'Angelo, Andrew. Analyzing the Gender Wage-gap in Ontario's Public Sector.

Degree: MS, School of Computer Science, 2016, University of Guelph

 The disparity in wages between men and women is a well known fact; however, the contribution of each known factor is not fully understood. Leveraging… (more)

Subjects/Keywords: gender wage-gap; data cleaning; data collection; machine learning; classification

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APA (6th Edition):

D'Angelo, A. (2016). Analyzing the Gender Wage-gap in Ontario's Public Sector. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9653

Chicago Manual of Style (16th Edition):

D'Angelo, Andrew. “Analyzing the Gender Wage-gap in Ontario's Public Sector.” 2016. Masters Thesis, University of Guelph. Accessed September 28, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9653.

MLA Handbook (7th Edition):

D'Angelo, Andrew. “Analyzing the Gender Wage-gap in Ontario's Public Sector.” 2016. Web. 28 Sep 2020.

Vancouver:

D'Angelo A. Analyzing the Gender Wage-gap in Ontario's Public Sector. [Internet] [Masters thesis]. University of Guelph; 2016. [cited 2020 Sep 28]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9653.

Council of Science Editors:

D'Angelo A. Analyzing the Gender Wage-gap in Ontario's Public Sector. [Masters Thesis]. University of Guelph; 2016. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/9653

28. Webb, Dean. Efficient piecewise linear classifiers and applications.

Degree: PhD, 2011, Federation University Australia

Supervised learning has become an essential part of data mining for industry, military, science and academia. Classification, a type of supervised learning allows a machine… (more)

Subjects/Keywords: Data mining; Data classification; Supervised learning; Artificial intelligence; Knowledge-based systems

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APA (6th Edition):

Webb, D. (2011). Efficient piecewise linear classifiers and applications. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668

Chicago Manual of Style (16th Edition):

Webb, Dean. “Efficient piecewise linear classifiers and applications.” 2011. Doctoral Dissertation, Federation University Australia. Accessed September 28, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668.

MLA Handbook (7th Edition):

Webb, Dean. “Efficient piecewise linear classifiers and applications.” 2011. Web. 28 Sep 2020.

Vancouver:

Webb D. Efficient piecewise linear classifiers and applications. [Internet] [Doctoral dissertation]. Federation University Australia; 2011. [cited 2020 Sep 28]. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668.

Council of Science Editors:

Webb D. Efficient piecewise linear classifiers and applications. [Doctoral Dissertation]. Federation University Australia; 2011. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668


University of Illinois – Urbana-Champaign

29. Tao, Fangbo. Text cube: construction, summarization and mining.

Degree: PhD, Computer Science, 2017, University of Illinois – Urbana-Champaign

 A large portion of real world data is either text or structured (\eg, relational) data. Such data objects are often linked together (\eg, structured product… (more)

Subjects/Keywords: Text cube; Data cube; Data mining; Natural language processing; Text classification

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APA (6th Edition):

Tao, F. (2017). Text cube: construction, summarization and mining. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99244

Chicago Manual of Style (16th Edition):

Tao, Fangbo. “Text cube: construction, summarization and mining.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed September 28, 2020. http://hdl.handle.net/2142/99244.

MLA Handbook (7th Edition):

Tao, Fangbo. “Text cube: construction, summarization and mining.” 2017. Web. 28 Sep 2020.

Vancouver:

Tao F. Text cube: construction, summarization and mining. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2142/99244.

Council of Science Editors:

Tao F. Text cube: construction, summarization and mining. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99244


University of Guelph

30. Alattas, Ali. Bagging Classification Trees for Longitudinal Data.

Degree: Master of Applied Science, Department of Mathematics and Statistics, 2019, University of Guelph

 Many studies handle binary longitudinal data by model-based classifiers, although their assumptions are often unsatisfied with real data. In contrast, tree-based classifiers are free of… (more)

Subjects/Keywords: Classification trees; Bagging; Longitudinal data; bootstrap; correlated data

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APA (6th Edition):

Alattas, A. (2019). Bagging Classification Trees for Longitudinal Data. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/14764

Chicago Manual of Style (16th Edition):

Alattas, Ali. “Bagging Classification Trees for Longitudinal Data.” 2019. Masters Thesis, University of Guelph. Accessed September 28, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/14764.

MLA Handbook (7th Edition):

Alattas, Ali. “Bagging Classification Trees for Longitudinal Data.” 2019. Web. 28 Sep 2020.

Vancouver:

Alattas A. Bagging Classification Trees for Longitudinal Data. [Internet] [Masters thesis]. University of Guelph; 2019. [cited 2020 Sep 28]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/14764.

Council of Science Editors:

Alattas A. Bagging Classification Trees for Longitudinal Data. [Masters Thesis]. University of Guelph; 2019. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/14764

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