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You searched for subject:(insolvency classification AND prediction). Showing records 1 – 30 of 265 total matches.

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1. Šinko, Mitja. Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov.

Degree: 2016, Univerza v Mariboru

Gradbeništvo v Sloveniji je doživelo pred krizo enormno rast. Rast so podpirali bančni finančni viri. Z nastopom krize so se začele težave na obeh koncih,… (more)

Subjects/Keywords: gradbeništvo; insolventnost; presojanje insolventnosti; napovedovanje insolventnosti; statistične metode; Altman; multipla diskriminantna analiza; logistična regresija; stroški napak; construction industry; insolvency; insolvency classification and prediction; statistical methods; Altman; MDA; logistic regression; error costs; info:eu-repo/classification/udc/519.2

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APA (6th Edition):

Šinko, M. (2016). Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov. (Masters Thesis). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=58264 ; https://dk.um.si/Dokument.php?id=101249&dn= ; https://plus.si.cobiss.net/opac7/bib/12643868?lang=sl

Chicago Manual of Style (16th Edition):

Šinko, Mitja. “Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov.” 2016. Masters Thesis, Univerza v Mariboru. Accessed July 09, 2020. https://dk.um.si/IzpisGradiva.php?id=58264 ; https://dk.um.si/Dokument.php?id=101249&dn= ; https://plus.si.cobiss.net/opac7/bib/12643868?lang=sl.

MLA Handbook (7th Edition):

Šinko, Mitja. “Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov.” 2016. Web. 09 Jul 2020.

Vancouver:

Šinko M. Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov. [Internet] [Masters thesis]. Univerza v Mariboru; 2016. [cited 2020 Jul 09]. Available from: https://dk.um.si/IzpisGradiva.php?id=58264 ; https://dk.um.si/Dokument.php?id=101249&dn= ; https://plus.si.cobiss.net/opac7/bib/12643868?lang=sl.

Council of Science Editors:

Šinko M. Presojanje in napovedovanje insolventnosti za panogo gradbeništvo s pomočjo statističnih modelov. [Masters Thesis]. Univerza v Mariboru; 2016. Available from: https://dk.um.si/IzpisGradiva.php?id=58264 ; https://dk.um.si/Dokument.php?id=101249&dn= ; https://plus.si.cobiss.net/opac7/bib/12643868?lang=sl


Boston University

2. Milton, Jacqueline Nicole. Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification.

Degree: PhD, Biostatistics, 2014, Boston University

 Sickle cell disease is a monogenic blood disorder in which the clinical course and disease severity vary widely among patients. In order for physicians to… (more)

Subjects/Keywords: Biostatistics; Correlated traits; Phenotype classification; Phenotype prediction

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APA (6th Edition):

Milton, J. N. (2014). Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/15987

Chicago Manual of Style (16th Edition):

Milton, Jacqueline Nicole. “Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification.” 2014. Doctoral Dissertation, Boston University. Accessed July 09, 2020. http://hdl.handle.net/2144/15987.

MLA Handbook (7th Edition):

Milton, Jacqueline Nicole. “Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification.” 2014. Web. 09 Jul 2020.

Vancouver:

Milton JN. Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification. [Internet] [Doctoral dissertation]. Boston University; 2014. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/2144/15987.

Council of Science Editors:

Milton JN. Identifying genetic variants associated with multiple correlated traits and the use of an ensemble of genetic risk models for phenotype prediction and classification. [Doctoral Dissertation]. Boston University; 2014. Available from: http://hdl.handle.net/2144/15987

3. Rezaeisarlak, Elham 1966-. CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS.

Degree: 2019, University of Saskatchewan

 Background: Juvenile idiopathic arthritis (JIA) comprises a heterogeneous group of conditions that share chronic arthritis as a common characteristic. Current classification criteria for chronic childhood… (more)

Subjects/Keywords: Chronic Childhood Arthritis; Biomarkers; Classification; Prediction

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APA (6th Edition):

Rezaeisarlak, E. 1. (2019). CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/12085

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):

Rezaeisarlak, Elham 1966-. “CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS.” 2019. Thesis, University of Saskatchewan. Accessed July 09, 2020. http://hdl.handle.net/10388/12085.

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

MLA Handbook (7th Edition):

Rezaeisarlak, Elham 1966-. “CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS.” 2019. Web. 09 Jul 2020.

Vancouver:

Rezaeisarlak E1. CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS. [Internet] [Thesis]. University of Saskatchewan; 2019. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10388/12085.

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

Council of Science Editors:

Rezaeisarlak E1. CLINICAL AND BIOLOGICALLY-BASED APPROACHES FOR CLASSIFYING AND PREDICTING EARLY OUTCOMES OF CHRONIC CHILDHOOD ARTHRITIS. [Thesis]. University of Saskatchewan; 2019. Available from: http://hdl.handle.net/10388/12085

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


University of Newcastle upon Tyne

4. Laws, David Joseph. A Bayes decision theoretic approach to the optimal design of screens.

Degree: PhD, 1997, University of Newcastle upon Tyne

 An item may be said to reach a standard suitable for use if it has some prescribed attributes. Supposet hat a variable 2: measurest he… (more)

Subjects/Keywords: 519.5; Prediction; Classification

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APA (6th Edition):

Laws, D. J. (1997). A Bayes decision theoretic approach to the optimal design of screens. (Doctoral Dissertation). University of Newcastle upon Tyne. Retrieved from http://theses.ncl.ac.uk/jspui/handle/10443/648 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363532

Chicago Manual of Style (16th Edition):

Laws, David Joseph. “A Bayes decision theoretic approach to the optimal design of screens.” 1997. Doctoral Dissertation, University of Newcastle upon Tyne. Accessed July 09, 2020. http://theses.ncl.ac.uk/jspui/handle/10443/648 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363532.

MLA Handbook (7th Edition):

Laws, David Joseph. “A Bayes decision theoretic approach to the optimal design of screens.” 1997. Web. 09 Jul 2020.

Vancouver:

Laws DJ. A Bayes decision theoretic approach to the optimal design of screens. [Internet] [Doctoral dissertation]. University of Newcastle upon Tyne; 1997. [cited 2020 Jul 09]. Available from: http://theses.ncl.ac.uk/jspui/handle/10443/648 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363532.

Council of Science Editors:

Laws DJ. A Bayes decision theoretic approach to the optimal design of screens. [Doctoral Dissertation]. University of Newcastle upon Tyne; 1997. Available from: http://theses.ncl.ac.uk/jspui/handle/10443/648 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363532


Kansas State University

5. 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 July 09, 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. 09 Jul 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 Jul 09]. 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


Delft University of Technology

6. Sarabchi, F. (author). Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach.

Degree: 2015, Delft University of Technology

Predicting the popularity of contents in social networks is quite important for several applications such as viral marketing, news propagation and personalization. In this work,… (more)

Subjects/Keywords: Twitter; popularity prediction; social networks; microblogging; classification

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APA (6th Edition):

Sarabchi, F. (. (2015). Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7b734e79-1a5c-4d08-95e5-81af22cd6237

Chicago Manual of Style (16th Edition):

Sarabchi, F (author). “Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach.” 2015. Masters Thesis, Delft University of Technology. Accessed July 09, 2020. http://resolver.tudelft.nl/uuid:7b734e79-1a5c-4d08-95e5-81af22cd6237.

MLA Handbook (7th Edition):

Sarabchi, F (author). “Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach.” 2015. Web. 09 Jul 2020.

Vancouver:

Sarabchi F(. Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Jul 09]. Available from: http://resolver.tudelft.nl/uuid:7b734e79-1a5c-4d08-95e5-81af22cd6237.

Council of Science Editors:

Sarabchi F(. Quantitative Prediction of Twitter Message Dissemination: A Machine Learning Approach. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:7b734e79-1a5c-4d08-95e5-81af22cd6237


University of Victoria

7. Khoshbakht, Saman. Improving processor power demand comprehension in data-driven power and software phase classification and prediction.

Degree: Department of Electrical and Computer Engineering, 2018, University of Victoria

 The single-core performance trend predicted by Moore's law has been impeded in recent years partly due to the limitations imposed by increasing processor power demands.… (more)

Subjects/Keywords: processor; power prediction; software phase classification; software phase prediction; data-driven power; processor power management

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APA (6th Edition):

Khoshbakht, S. (2018). Improving processor power demand comprehension in data-driven power and software phase classification and prediction. (Thesis). University of Victoria. Retrieved from https://dspace.library.uvic.ca//handle/1828/9903

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):

Khoshbakht, Saman. “Improving processor power demand comprehension in data-driven power and software phase classification and prediction.” 2018. Thesis, University of Victoria. Accessed July 09, 2020. https://dspace.library.uvic.ca//handle/1828/9903.

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

MLA Handbook (7th Edition):

Khoshbakht, Saman. “Improving processor power demand comprehension in data-driven power and software phase classification and prediction.” 2018. Web. 09 Jul 2020.

Vancouver:

Khoshbakht S. Improving processor power demand comprehension in data-driven power and software phase classification and prediction. [Internet] [Thesis]. University of Victoria; 2018. [cited 2020 Jul 09]. Available from: https://dspace.library.uvic.ca//handle/1828/9903.

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

Council of Science Editors:

Khoshbakht S. Improving processor power demand comprehension in data-driven power and software phase classification and prediction. [Thesis]. University of Victoria; 2018. Available from: https://dspace.library.uvic.ca//handle/1828/9903

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


Wayne State University

8. Al aydie, Noor. Hierarchical multi-label classification for protein function prediction going beyond traditional approaches.

Degree: PhD, Computer Science, 2012, Wayne State University

  Hierarchical multi-label classification is a variant of traditional classification in which the instances can belong to several labels, that are in turn organized in… (more)

Subjects/Keywords: classification, gene function prediction, Hierarchical classification, integraion, multi-label, protein function classification; Bioinformatics; Computer Sciences

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APA (6th Edition):

Al aydie, N. (2012). Hierarchical multi-label classification for protein function prediction going beyond traditional approaches. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/525

Chicago Manual of Style (16th Edition):

Al aydie, Noor. “Hierarchical multi-label classification for protein function prediction going beyond traditional approaches.” 2012. Doctoral Dissertation, Wayne State University. Accessed July 09, 2020. https://digitalcommons.wayne.edu/oa_dissertations/525.

MLA Handbook (7th Edition):

Al aydie, Noor. “Hierarchical multi-label classification for protein function prediction going beyond traditional approaches.” 2012. Web. 09 Jul 2020.

Vancouver:

Al aydie N. Hierarchical multi-label classification for protein function prediction going beyond traditional approaches. [Internet] [Doctoral dissertation]. Wayne State University; 2012. [cited 2020 Jul 09]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/525.

Council of Science Editors:

Al aydie N. Hierarchical multi-label classification for protein function prediction going beyond traditional approaches. [Doctoral Dissertation]. Wayne State University; 2012. Available from: https://digitalcommons.wayne.edu/oa_dissertations/525

9. Alhajjar, Yasser. Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal.

Degree: Docteur es, Informatique, 2017, Angers

L’électroencéphalogramme (EEG), mesure de l'activité électrique du cerveau, reste une des meilleures méthodes de prévision non-invasive des résultats neurologiques. L'objectif de notre travail est de… (more)

Subjects/Keywords: Prévision; Classification; Amplitude EEG; Corrélation; Preterm; Electroencephalogram; Prediction; Classification; EEG amplitude; Correlation; 004

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APA (6th Edition):

Alhajjar, Y. (2017). Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal. (Doctoral Dissertation). Angers. Retrieved from http://www.theses.fr/2017ANGE0020

Chicago Manual of Style (16th Edition):

Alhajjar, Yasser. “Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal.” 2017. Doctoral Dissertation, Angers. Accessed July 09, 2020. http://www.theses.fr/2017ANGE0020.

MLA Handbook (7th Edition):

Alhajjar, Yasser. “Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal.” 2017. Web. 09 Jul 2020.

Vancouver:

Alhajjar Y. Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal. [Internet] [Doctoral dissertation]. Angers; 2017. [cited 2020 Jul 09]. Available from: http://www.theses.fr/2017ANGE0020.

Council of Science Editors:

Alhajjar Y. Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG : Prediction of neuro-developmental risk of premature newborn by automatic classification of the EEG signal. [Doctoral Dissertation]. Angers; 2017. Available from: http://www.theses.fr/2017ANGE0020


University of New South Wales

10. Lekamge, Basnayake Mudiyanselage Thamali. An automated approach for analysing, classifying and predicting protein x-ray crystallography images.

Degree: Computer Science & Engineering, 2016, University of New South Wales

 Protein X-ray crystallography images are usually studied for the purpose of identifying the three-dimensional structure of proteins at atomic resolution. These experiments produce hundreds and… (more)

Subjects/Keywords: Random forests; Protein X-Ray Crystallography; Classification; Multi-view learning; Sequence classification; Sequence prediction

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APA (6th Edition):

Lekamge, B. M. T. (2016). An automated approach for analysing, classifying and predicting protein x-ray crystallography images. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58814 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47628/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Lekamge, Basnayake Mudiyanselage Thamali. “An automated approach for analysing, classifying and predicting protein x-ray crystallography images.” 2016. Doctoral Dissertation, University of New South Wales. Accessed July 09, 2020. http://handle.unsw.edu.au/1959.4/58814 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47628/SOURCE02?view=true.

MLA Handbook (7th Edition):

Lekamge, Basnayake Mudiyanselage Thamali. “An automated approach for analysing, classifying and predicting protein x-ray crystallography images.” 2016. Web. 09 Jul 2020.

Vancouver:

Lekamge BMT. An automated approach for analysing, classifying and predicting protein x-ray crystallography images. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2020 Jul 09]. Available from: http://handle.unsw.edu.au/1959.4/58814 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47628/SOURCE02?view=true.

Council of Science Editors:

Lekamge BMT. An automated approach for analysing, classifying and predicting protein x-ray crystallography images. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/58814 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47628/SOURCE02?view=true


Dalhousie University

11. Rendon, Ricardo Andres. A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT.

Degree: MS, Department of Community Health & Epidemiology, 2012, Dalhousie University

 Objective: To develop a predictive model for preoperative differentiation between benign (B) and malignant (M) histology in patients with renal masses (RM) using recursive partitioning.… (more)

Subjects/Keywords: Renal cell carcinoma; Renal mass; prediction; Classification tree

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APA (6th Edition):

Rendon, R. A. (2012). A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT. (Masters Thesis). Dalhousie University. Retrieved from http://hdl.handle.net/10222/15337

Chicago Manual of Style (16th Edition):

Rendon, Ricardo Andres. “A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT.” 2012. Masters Thesis, Dalhousie University. Accessed July 09, 2020. http://hdl.handle.net/10222/15337.

MLA Handbook (7th Edition):

Rendon, Ricardo Andres. “A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT.” 2012. Web. 09 Jul 2020.

Vancouver:

Rendon RA. A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT. [Internet] [Masters thesis]. Dalhousie University; 2012. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10222/15337.

Council of Science Editors:

Rendon RA. A PRE-OPERATIVE PREDICTIVE MODEL FOR THE CLASSIFICATION OF NEWLY DIAGNOSED RENAL MASSES LESS THAN 5 CM IN DIAMETER AS BENIGN OR MALIGNANT. [Masters Thesis]. Dalhousie University; 2012. Available from: http://hdl.handle.net/10222/15337


Universidade Nova

12. Fonseca, João Gonçalo Silva Serra. March madness prediction using machine learning techniques.

Degree: 2018, Universidade Nova

 March Madness describes the final tournament of the college basketball championship, considered by many as the biggest sporting event in the United States - moving… (more)

Subjects/Keywords: March Madness; NCAAB; Basketball; Prediction; Classification problem; Machine Learning

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APA (6th Edition):

Fonseca, J. G. S. S. (2018). March madness prediction using machine learning techniques. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/33864

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):

Fonseca, João Gonçalo Silva Serra. “March madness prediction using machine learning techniques.” 2018. Thesis, Universidade Nova. Accessed July 09, 2020. https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/33864.

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

MLA Handbook (7th Edition):

Fonseca, João Gonçalo Silva Serra. “March madness prediction using machine learning techniques.” 2018. Web. 09 Jul 2020.

Vancouver:

Fonseca JGSS. March madness prediction using machine learning techniques. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2020 Jul 09]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/33864.

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

Council of Science Editors:

Fonseca JGSS. March madness prediction using machine learning techniques. [Thesis]. Universidade Nova; 2018. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/33864

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


University of Waterloo

13. Jiang, Tian. Personalized Defect Prediction.

Degree: 2013, University of Waterloo

 Academia and industry expend much effort to predict software defects. Researchers proposed many defect prediction algorithms and metrics. While previous defect prediction techniques often take… (more)

Subjects/Keywords: change classification; defect prediction; machine learning; software reliability

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APA (6th Edition):

Jiang, T. (2013). Personalized Defect Prediction. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7786

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, Tian. “Personalized Defect Prediction.” 2013. Thesis, University of Waterloo. Accessed July 09, 2020. http://hdl.handle.net/10012/7786.

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

MLA Handbook (7th Edition):

Jiang, Tian. “Personalized Defect Prediction.” 2013. Web. 09 Jul 2020.

Vancouver:

Jiang T. Personalized Defect Prediction. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10012/7786.

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

Council of Science Editors:

Jiang T. Personalized Defect Prediction. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7786

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


University of Illinois – Chicago

14. Wang, Hong. Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics.

Degree: 2017, University of Illinois – Chicago

 Many Information Retrieval (IR) and Natural Language Processing (NLP) tasks require predicting structured objects (e.g., sequences, rankings, matchings, parse trees) that are evaluated using F-score… (more)

Subjects/Keywords: Adversarial classification; structure prediction; Information Retrieval and Natural Language Processing application

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

APA (6th Edition):

Wang, H. (2017). Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21860

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

Chicago Manual of Style (16th Edition):

Wang, Hong. “Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics.” 2017. Thesis, University of Illinois – Chicago. Accessed July 09, 2020. http://hdl.handle.net/10027/21860.

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

MLA Handbook (7th Edition):

Wang, Hong. “Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics.” 2017. Web. 09 Jul 2020.

Vancouver:

Wang H. Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10027/21860.

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

Council of Science Editors:

Wang H. Adversarial Prediction Framework for Information Retrieval and Natural Language Processing Metrics. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21860

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


University of Illinois – Chicago

15. Fathony, Rizal Zaini Ahmad. Performance-Aligned Learning Algorithms with Statistical Guarantees.

Degree: 2019, University of Illinois – Chicago

 The goal of many prediction tasks in machine learning is to learn a prediction function that minimizes certain loss metrics (e.g., zero-one, ordinal, and cost-sensitive… (more)

Subjects/Keywords: Machine Learning; Classification; Surrogate Loss; Structured Prediction; Graphical Models

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APA (6th Edition):

Fathony, R. Z. A. (2019). Performance-Aligned Learning Algorithms with Statistical Guarantees. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/23627

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):

Fathony, Rizal Zaini Ahmad. “Performance-Aligned Learning Algorithms with Statistical Guarantees.” 2019. Thesis, University of Illinois – Chicago. Accessed July 09, 2020. http://hdl.handle.net/10027/23627.

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

MLA Handbook (7th Edition):

Fathony, Rizal Zaini Ahmad. “Performance-Aligned Learning Algorithms with Statistical Guarantees.” 2019. Web. 09 Jul 2020.

Vancouver:

Fathony RZA. Performance-Aligned Learning Algorithms with Statistical Guarantees. [Internet] [Thesis]. University of Illinois – Chicago; 2019. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10027/23627.

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

Council of Science Editors:

Fathony RZA. Performance-Aligned Learning Algorithms with Statistical Guarantees. [Thesis]. University of Illinois – Chicago; 2019. Available from: http://hdl.handle.net/10027/23627

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


Georgia State University

16. Wang, Xiaoyuan. Data Mining Analysis of the Parkinson's Disease.

Degree: MS, Mathematics and Statistics, 2014, Georgia State University

  Biological research is becoming increasingly database driven and statistical learning can be used to discover patterns in the biological data. In the thesis, the… (more)

Subjects/Keywords: Supervised Learning; Cross Validation; Prediction Analysis; Classification; Model Selection; ROC Curve

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

APA (6th Edition):

Wang, X. (2014). Data Mining Analysis of the Parkinson's Disease. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/math_theses/143

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

Chicago Manual of Style (16th Edition):

Wang, Xiaoyuan. “Data Mining Analysis of the Parkinson's Disease.” 2014. Thesis, Georgia State University. Accessed July 09, 2020. https://scholarworks.gsu.edu/math_theses/143.

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

MLA Handbook (7th Edition):

Wang, Xiaoyuan. “Data Mining Analysis of the Parkinson's Disease.” 2014. Web. 09 Jul 2020.

Vancouver:

Wang X. Data Mining Analysis of the Parkinson's Disease. [Internet] [Thesis]. Georgia State University; 2014. [cited 2020 Jul 09]. Available from: https://scholarworks.gsu.edu/math_theses/143.

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

Council of Science Editors:

Wang X. Data Mining Analysis of the Parkinson's Disease. [Thesis]. Georgia State University; 2014. Available from: https://scholarworks.gsu.edu/math_theses/143

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


Iowa State University

17. Lanker, Cory Lee. Local prediction and classification techniques for machine learning and data mining.

Degree: 2015, Iowa State University

 A variety of conditional probability models estimate the regression or class probability function for the purpose of prediction or classification. Bayesian mixture models provide flexible… (more)

Subjects/Keywords: Statistics; Bayesian; categorical predictor; classification; mixture model; prediction; Statistics and Probability

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

APA (6th Edition):

Lanker, C. L. (2015). Local prediction and classification techniques for machine learning and data mining. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/14404

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):

Lanker, Cory Lee. “Local prediction and classification techniques for machine learning and data mining.” 2015. Thesis, Iowa State University. Accessed July 09, 2020. https://lib.dr.iastate.edu/etd/14404.

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

MLA Handbook (7th Edition):

Lanker, Cory Lee. “Local prediction and classification techniques for machine learning and data mining.” 2015. Web. 09 Jul 2020.

Vancouver:

Lanker CL. Local prediction and classification techniques for machine learning and data mining. [Internet] [Thesis]. Iowa State University; 2015. [cited 2020 Jul 09]. Available from: https://lib.dr.iastate.edu/etd/14404.

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

Council of Science Editors:

Lanker CL. Local prediction and classification techniques for machine learning and data mining. [Thesis]. Iowa State University; 2015. Available from: https://lib.dr.iastate.edu/etd/14404

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


Uppsala University

18. Öhrn, Håkan. General image classifier for fluorescence microscopy using transfer learning.

Degree: Division of Visual Information and Interaction, 2019, Uppsala University

  Modern microscopy and automation technologies enable experiments which can produce millions of images each day. The valuable information is often sparse, and requires clever… (more)

Subjects/Keywords: transfer learning; conformal prediction; image classification; Computer Sciences; Datavetenskap (datalogi)

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

APA (6th Edition):

Öhrn, H. (2019). General image classifier for fluorescence microscopy using transfer learning. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388633

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):

Öhrn, Håkan. “General image classifier for fluorescence microscopy using transfer learning.” 2019. Thesis, Uppsala University. Accessed July 09, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388633.

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

MLA Handbook (7th Edition):

Öhrn, Håkan. “General image classifier for fluorescence microscopy using transfer learning.” 2019. Web. 09 Jul 2020.

Vancouver:

Öhrn H. General image classifier for fluorescence microscopy using transfer learning. [Internet] [Thesis]. Uppsala University; 2019. [cited 2020 Jul 09]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388633.

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

Council of Science Editors:

Öhrn H. General image classifier for fluorescence microscopy using transfer learning. [Thesis]. Uppsala University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388633

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


University of Notre Dame

19. Ryan N. Lichtenwalter. Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>.

Degree: Computer Science and Engineering, 2012, University of Notre Dame

  Link prediction is succinctly stated as identifying unobserved links in a network. It has important applications ranging from recommending beneficial relationships in social networks… (more)

Subjects/Keywords: link analysis; data mining; link prediction; networks; graph theory; classification

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APA (6th Edition):

Lichtenwalter, R. N. (2012). Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/fj23611103z

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):

Lichtenwalter, Ryan N.. “Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>.” 2012. Thesis, University of Notre Dame. Accessed July 09, 2020. https://curate.nd.edu/show/fj23611103z.

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

MLA Handbook (7th Edition):

Lichtenwalter, Ryan N.. “Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>.” 2012. Web. 09 Jul 2020.

Vancouver:

Lichtenwalter RN. Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2020 Jul 09]. Available from: https://curate.nd.edu/show/fj23611103z.

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

Council of Science Editors:

Lichtenwalter RN. Network Analysis and Link Prediction: Effective and Meaningful Modeling and Evaluation</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/fj23611103z

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


University of the Western Cape

20. Mehl, Daniel James Gustav. An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas .

Degree: 2019, University of the Western Cape

 Improved knowledge is required on the quantity and source of water resources, particularly evident during periods of drought currently being faced in South Africa. There… (more)

Subjects/Keywords: Heuningnes Catchment; Flood prediction; Ungauged catchments; Wetland classification

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

APA (6th Edition):

Mehl, D. J. G. (2019). An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas . (Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/7031

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):

Mehl, Daniel James Gustav. “An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas .” 2019. Thesis, University of the Western Cape. Accessed July 09, 2020. http://hdl.handle.net/11394/7031.

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

MLA Handbook (7th Edition):

Mehl, Daniel James Gustav. “An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas .” 2019. Web. 09 Jul 2020.

Vancouver:

Mehl DJG. An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas . [Internet] [Thesis]. University of the Western Cape; 2019. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/11394/7031.

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

Council of Science Editors:

Mehl DJG. An assessment of the river flow contributions of tributaries and the effects of floodplain wetlands on spatio-temporal variations of river flows in the Nuwejaars river catchment, Cape Agulhas . [Thesis]. University of the Western Cape; 2019. Available from: http://hdl.handle.net/11394/7031

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


Texas Tech University

21. Gard, Dwight R. Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees).

Degree: College of Education, 2011, Texas Tech University

 Previous research efforts concerning teacher certification in Texas focused primarily on the Pedagogy and Professional Responsibilities exam; an exam that all teacher candidates must pass… (more)

Subjects/Keywords: Teacher certification; Certification exam; TExES exam; Prediction; Classification trees; Nonparametric

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

APA (6th Edition):

Gard, D. R. (2011). Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees). (Thesis). Texas Tech University. Retrieved from http://hdl.handle.net/2346/ETD-TTU-2011-05-1331

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):

Gard, Dwight R. “Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees).” 2011. Thesis, Texas Tech University. Accessed July 09, 2020. http://hdl.handle.net/2346/ETD-TTU-2011-05-1331.

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

MLA Handbook (7th Edition):

Gard, Dwight R. “Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees).” 2011. Web. 09 Jul 2020.

Vancouver:

Gard DR. Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees). [Internet] [Thesis]. Texas Tech University; 2011. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/2346/ETD-TTU-2011-05-1331.

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

Council of Science Editors:

Gard DR. Teacher Certification Exams: Predicting Failure on the TExES History (8-12) Content Exam (A Nonparametric Approach using Classification Trees). [Thesis]. Texas Tech University; 2011. Available from: http://hdl.handle.net/2346/ETD-TTU-2011-05-1331

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


University of Tennessee – Knoxville

22. Khan, Mohammad Hashir. A CNN-LSTM for predicting mortality in the ICU.

Degree: MS, Industrial Engineering, 2019, University of Tennessee – Knoxville

 An accurate predicted mortality is crucial to healthcare as it provides an empirical risk estimate for prognostic decision making, patient stratification and hospital benchmarking. Current… (more)

Subjects/Keywords: deep learning; neural networks; mortality prediction; text classification

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

APA (6th Edition):

Khan, M. H. (2019). A CNN-LSTM for predicting mortality in the ICU. (Thesis). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_gradthes/5441

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):

Khan, Mohammad Hashir. “A CNN-LSTM for predicting mortality in the ICU.” 2019. Thesis, University of Tennessee – Knoxville. Accessed July 09, 2020. https://trace.tennessee.edu/utk_gradthes/5441.

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

MLA Handbook (7th Edition):

Khan, Mohammad Hashir. “A CNN-LSTM for predicting mortality in the ICU.” 2019. Web. 09 Jul 2020.

Vancouver:

Khan MH. A CNN-LSTM for predicting mortality in the ICU. [Internet] [Thesis]. University of Tennessee – Knoxville; 2019. [cited 2020 Jul 09]. Available from: https://trace.tennessee.edu/utk_gradthes/5441.

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

Council of Science Editors:

Khan MH. A CNN-LSTM for predicting mortality in the ICU. [Thesis]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_gradthes/5441

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


University of Pretoria

23. 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 (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 July 09, 2020. http://hdl.handle.net/2263/27624.

MLA Handbook (7th Edition):

Van der Walt, Christiaan. “Data measures that characterise classification problems.” 2008. Web. 09 Jul 2020.

Vancouver:

Van der Walt C. Data measures that characterise classification problems. [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Jul 09]. 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

24. [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 July 09, 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. 09 Jul 2020.

Vancouver:

author] [. Data measures that characterise classification problems . [Internet] [Masters thesis]. University of Pretoria; 2008. [cited 2020 Jul 09]. 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/


Queensland University of Technology

25. Ferroudj, Meriem. Detection of rain in acoustic recordings of the environment using machine learning techniques.

Degree: 2015, Queensland University of Technology

This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.

Subjects/Keywords: Environmental sound classification; Feature extraction; Acoustic event classification; Audio classification; Machine learning; Data mining; Prediction techniques

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

APA (6th Edition):

Ferroudj, M. (2015). Detection of rain in acoustic recordings of the environment using machine learning techniques. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/82848/

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):

Ferroudj, Meriem. “Detection of rain in acoustic recordings of the environment using machine learning techniques.” 2015. Thesis, Queensland University of Technology. Accessed July 09, 2020. https://eprints.qut.edu.au/82848/.

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

MLA Handbook (7th Edition):

Ferroudj, Meriem. “Detection of rain in acoustic recordings of the environment using machine learning techniques.” 2015. Web. 09 Jul 2020.

Vancouver:

Ferroudj M. Detection of rain in acoustic recordings of the environment using machine learning techniques. [Internet] [Thesis]. Queensland University of Technology; 2015. [cited 2020 Jul 09]. Available from: https://eprints.qut.edu.au/82848/.

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

Council of Science Editors:

Ferroudj M. Detection of rain in acoustic recordings of the environment using machine learning techniques. [Thesis]. Queensland University of Technology; 2015. Available from: https://eprints.qut.edu.au/82848/

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


University of Bradford

26. Sowan, Bilal Ibrahim. Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base.

Degree: PhD, 2011, University of Bradford

 Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining.… (more)

Subjects/Keywords: 502.85; Prediction; Fuzzy associative rule mining; Fuzzy clustering; Associative classification rule base; Data mining; Predictive model; Decision support system; Minimizing prediction error; Prediction performance

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

APA (6th Edition):

Sowan, B. I. (2011). Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base. (Doctoral Dissertation). University of Bradford. Retrieved from http://hdl.handle.net/10454/5387

Chicago Manual of Style (16th Edition):

Sowan, Bilal Ibrahim. “Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base.” 2011. Doctoral Dissertation, University of Bradford. Accessed July 09, 2020. http://hdl.handle.net/10454/5387.

MLA Handbook (7th Edition):

Sowan, Bilal Ibrahim. “Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base.” 2011. Web. 09 Jul 2020.

Vancouver:

Sowan BI. Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base. [Internet] [Doctoral dissertation]. University of Bradford; 2011. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10454/5387.

Council of Science Editors:

Sowan BI. Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base. [Doctoral Dissertation]. University of Bradford; 2011. Available from: http://hdl.handle.net/10454/5387


Universidade do Rio Grande do Sul

27. Tumitan, Diego Costa. Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro.

Degree: 2014, Universidade do Rio Grande do Sul

O sucesso da mineração de opiniões para processar automaticamente grandes quantidades de conteúdo opinativo disponíveis na Internet tem sido demonstrado como uma solução de baixa… (more)

Subjects/Keywords: Recuperacao : Informacao; Opinion mining; Pesquisa operacional; Sentiment-based prediction; Sentiment classification; User-generated content

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

APA (6th Edition):

Tumitan, D. C. (2014). Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/134373

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):

Tumitan, Diego Costa. “Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro.” 2014. Thesis, Universidade do Rio Grande do Sul. Accessed July 09, 2020. http://hdl.handle.net/10183/134373.

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

MLA Handbook (7th Edition):

Tumitan, Diego Costa. “Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro.” 2014. Web. 09 Jul 2020.

Vancouver:

Tumitan DC. Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2014. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10183/134373.

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

Council of Science Editors:

Tumitan DC. Atributos discriminantes baseados em sentimento para a predição de pesquisas eleitorais : um estudo de caso no cenário brasileiro. [Thesis]. Universidade do Rio Grande do Sul; 2014. Available from: http://hdl.handle.net/10183/134373

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


Universidade do Rio Grande do Sul

28. Giacomel, Felipe dos Santos. Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações.

Degree: 2016, Universidade do Rio Grande do Sul

A previsão de séries temporais financeiras tem sido um tópico popular da literatura nos últimos anos. Contudo, embora muitos estudos de previsão de séries temporais… (more)

Subjects/Keywords: Neural networks; Redes neurais; Classification; Banco : Dados; Prediction; Stock markets; Time series

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

APA (6th Edition):

Giacomel, F. d. S. (2016). Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/134586

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):

Giacomel, Felipe dos Santos. “Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações.” 2016. Thesis, Universidade do Rio Grande do Sul. Accessed July 09, 2020. http://hdl.handle.net/10183/134586.

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

MLA Handbook (7th Edition):

Giacomel, Felipe dos Santos. “Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações.” 2016. Web. 09 Jul 2020.

Vancouver:

Giacomel FdS. Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2016. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10183/134586.

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

Council of Science Editors:

Giacomel FdS. Um método algorítmico para operações na bolsa de valores baseado em ensembles de redes neurais para modelar e prever os movimentos dos mercados de ações. [Thesis]. Universidade do Rio Grande do Sul; 2016. Available from: http://hdl.handle.net/10183/134586

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


Universidade do Rio Grande do Sul

29. Kahmann, Alessandro. Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade.

Degree: 2017, Universidade do Rio Grande do Sul

Nos últimos anos, a espectroscopia no infravermelho (IR) ganhou grande aceitação em diversas áreas de pesquisa por ser uma técnica rápida, simples e não destrutiva… (more)

Subjects/Keywords: Wavenumber selection; Seleção de comprimentos de onda; Espectroscopia no infravermelho; NIR; FTIR; Classification; Prediction

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

APA (6th Edition):

Kahmann, A. (2017). Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/178773

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):

Kahmann, Alessandro. “Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade.” 2017. Thesis, Universidade do Rio Grande do Sul. Accessed July 09, 2020. http://hdl.handle.net/10183/178773.

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

MLA Handbook (7th Edition):

Kahmann, Alessandro. “Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade.” 2017. Web. 09 Jul 2020.

Vancouver:

Kahmann A. Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2017. [cited 2020 Jul 09]. Available from: http://hdl.handle.net/10183/178773.

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

Council of Science Editors:

Kahmann A. Seleção de variáveis em dados de espectroscopia no infravermelho para controle de qualidade. [Thesis]. Universidade do Rio Grande do Sul; 2017. Available from: http://hdl.handle.net/10183/178773

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


Wright State University

30. Taslimitehrani, Vahid. Contrast Pattern Aided Regression and Classification.

Degree: PhD, Computer Science and Engineering PhD, 2016, Wright State University

 Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification(more)

Subjects/Keywords: Computer Science; Regression, Classification, Contrast pattern, Prediction, Data Mining, Machine Learning, Multi-dimensional data analysis

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

APA (6th Edition):

Taslimitehrani, V. (2016). Contrast Pattern Aided Regression and Classification. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1459377694

Chicago Manual of Style (16th Edition):

Taslimitehrani, Vahid. “Contrast Pattern Aided Regression and Classification.” 2016. Doctoral Dissertation, Wright State University. Accessed July 09, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1459377694.

MLA Handbook (7th Edition):

Taslimitehrani, Vahid. “Contrast Pattern Aided Regression and Classification.” 2016. Web. 09 Jul 2020.

Vancouver:

Taslimitehrani V. Contrast Pattern Aided Regression and Classification. [Internet] [Doctoral dissertation]. Wright State University; 2016. [cited 2020 Jul 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1459377694.

Council of Science Editors:

Taslimitehrani V. Contrast Pattern Aided Regression and Classification. [Doctoral Dissertation]. Wright State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1459377694

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