Research

1. M.M.C. Lamont, Assessing the influence of observations on the generalization performance of the kernel Fisher discriminant classifier, PhD thesis, Department of Statistics and Actuarial Science, Stellenbosch University, 2008. URL: http://hdl.handle.net/10019.1/1498  

2. M.M.C. Lamont, Binary classification trees: A comparison with popular classification methods in statistics using different software, M.Com thesis, Department of Statistics and Actuarial Science, Stellenbosch University, 2002. URL: http://hdl.handle.net/10019.1/52718

1. N. Louw, M. Lamont and S.J. Steel, Identification of Influential cases in Kernel Fisher Discriminant Analysis, in Complex Models and Computational Intensive Methods for Estimation and Prediction, 2007, CLEUP EDITORE, p. 296-301.

2. N. Louw, S.J. Steel and M.M.C. Lamont, Identification of influential cases in kernel Fisher discriminant analysis, Communications in Statistics - Simulation and Computation, vol. 37, no. 10, 2008, p. 2050 – 2062.

3. N. Louw, S. Steel and M. Lamont, Identification of Influential cases in Kernel Fisher Discriminant Analysis by using the Smallest Enclosing Hypersphere, in Advances in Data Analysis, Data handling and Business Intelligence, Springer-Verlag, 2010, p. 363-371.

1. J.A. van der Merwe, M. Nielsen and M. Lamont, Effect of Semperfresh on the post-storage quality of Packham’s Triumph pears and Golden Delicious apples, South African Fruit Journal, Jun/Jul 2002, p. 47-52.

2. C.J.C. Muller, W.J. Burger and M.M.C. Lamont, The live weight of replacement heifers in commercial dairy herds in the South Western Cape, Elsenburg Journal, vol. 11, no. 2, 2014, p. 45-51.

1. Nombasa Ntushelo, Exploratory and inferential multivariate statistical techniques for multidimensional count and binary data with applications in R, 2011. URL: http://hdl.handle.net/10019.1/17949  

2. Stephan van der Westhuizen, Nearest hypersphere classification: a comparison with other classification techniques, 2014. 

URL: http://hdl.handle.net/10019.1/95839

3. Hildegard Meyer (née Erasmus), Evaluating the performance of modern regression techniques with applications in R, 2015. 

4. Frances Coetzer, Aspects of multi-class nearest hypersphere classification, 2017. URL: http://hdl.handle.net/10019.1/102662

5. Agrippa Stulumani, Classification in high-dimensional data using sparse techniques, 2019. 

URL: http://hdl.handle.net/10019.1/105792

6. Zander Wessels, Interpreting decision boundaries of deep neural networks, 2019. 

URL: http://hdl.handle.net/10019.1/107202, Code: https://github.com/zanderbraam/vitrify

7. Willem Pretorius, A deep convolutional neural network architecture for image classification, 2020.

URL:http://hdl.handle.net/10019.1/109135 , Code: https://github.com/wlpretorius/Thesis

8. Jean-Pierre Bezuidenhout, Exploring the class imbalance problem in text classification, 2023. 

URL: http://hdl.handle.net/10019.1/126960 , Code : https://github.com/jeanpierrebez/MCom-Statistics-Thesis

9. Peter Manefeldt, Visualising interpretability in random forests, currently.

1. Benjamin Gurr, Prediction accuracy in linear regression and linear discriminant analysis, 2013.

2. Frances Coetzer, General purpose optimization in R with applications in support vector machine classification, 2015.

3. André Storm, Functions in R for support vector classification and hyper-parameter estimation, 2016.

4. Carlyle McCready, General purpose optimization in R with applications in kernel Fisher discriminant analysis, 2016.

5. Heinrich Thetard & Jarred Reyneke, Exploration of multi-class support vector machine, 2017.

6. Ntebogeng Mosime, General purpose optimization in R with applications in support vector regression, 2018.

7. Zoë Adams & Murray du Preez, Classification and variable selection with high-dimensional data, 2018.

8. Ulrich Kotze & Ishmat Ahmed, Pre-processing methods and sparse classification for high-dimensional cancer data, 2019.

9. Malissa Davids & Malissa Swindon, Exploring methods for improving the classification accuracy of classifiers with imbalanced data, 2020. 

10. Bongani Babeli & Tendai Musendo, Determining the impact of training load on distance running performance using machine learning techniques, 2021. 

11. Nomzamo Nyoni & Lebogang Omoregie, A quantitative analysis using various classification methods to predict clients’ participation in credit offerings, 2021. 

12. Luke Harding & Bea Scheepers, Client selection for marketing using the supervised learning method of classification, 2022. 

13. Sunal Kumar, A classification study to predict flight delays, 2023.

1. N Louw, S.J. Steel and M.M.C. Lamont, “Outlier identification in kernel Fisher discriminant analysis”, 26th European Meeting of Statisticians, Torún, Poland, July 24, 2006.

2. N. Louw, S.J. Steel and M.M.C. Lamont, “Identification of influential cases in kernel Fisher discriminant analysis”, S.Co.2007, Venice, Italy, September 6-8, 2007.

3. N. Louw, S.J. Steel and M.M.C. Lamont, “Identifying atypical cases in kernel Fisher discriminant analysis by using the smallest enclosing hypersphere”, German Classification Society 32nd Annual Conference, Hamburg, Germany, July 16-18, 2008.

4. N. Louw and M.M.C. Lamont, “Robust kernel Fisher discriminant analysis”, ICORS09 -International Conference on Robust Statistics, Italy, June 14-19, 2009.

5. N. Ntushelo, M.M.C. Lamont and M.I. Ferreira, “Exploratory multivariate statistical techniques for multidimensional data with applications in R”, The 26th International Biometric Conference (IBC), Kobe, Japan, August 26-31, 2012 – poster presentation. (Prize)

6. M.M.C. Lamont, “Classification with hyperspheres”, The International Federation of Classification Societies (IFCS), Tilburg University, The Netherlands, July 14-17, 2013.

7. Blignaut, R.J., Latief, A., Lombard-Jacobus, R., Luus, R., Lamont, M., Jacobs, R., “Western Cape Maths4Stats training 2012 to 2014 – valuable or not?”, The Tenth Southern Hemisphere Conference on the Teaching and Learning of Undergraduate Mathematics and Statistics, Port Elizabeth, South Africa, November 22-27, 2015. 

8. Manefeldt, P.C., Lamont, M.M.C, Lubbe, S., “Visualising interpretability of random forest models”, Joint conference of Data Science, Statistics & Visualisation (DSSV) and the European Conference on Data Analysis (ECDA), Antwerp, Belgium, 5-7 July, 2023

1. M.M.C. Lamont, N. Louw and S.J. Steel, “Outliers and influential observations in Fisher’s linear discriminant analysis and kernel Fisher discriminant analysis”, Stellenbosch University, Colloquium: Statistics Department, 15 October, 2004.

2. M.M.C. Lamont, N. Louw and S.J. Steel, “Aspects of kernel Fisher discriminant analysis”, Annual conference of the South African Statistical Association, Grahamstown, Rhodes University, 4 November, 2005.

3. M.M.C. Lamont, N. Louw and S.J. Steel, “The effect of outliers on the performance of kernel Fisher discriminant analysis”, Annual conference of the South African Statistical Association, Stellenbosch, Stellenbosch University, 1 November, 2006.

4. N. Louw, S.J. Steel and M.M.C. Lamont, “Identification of influential cases in kernel Fisher discriminant analysis”, Annual conference of the South African Statistical Association, Stellenbosch, Stellenbosch University, 1 November, 2006.

5. M.M.C. Lamont, “Assessing the influence of observations on the generalization performance of the kernel Fisher discriminant classifier”, PhD oral examination, Stellenbosch University, 11 August, 2008.

6. M.M.C. Lamont, “Statistical applications of the smallest enclosing hypersphere”, Stellenbosch University, Colloquium: Statistics Department, August 29, 2008.

7. M.M.C. Lamont, “Statistical applications of the smallest enclosing hypersphere”, Stellenbosch University, Vision and Learning group – Seminars: Computer Science Department, 7 November, 2008.

8. S. Steel, N. Louw, S. Bierman and M. Lamont, “Dimension reduction, modern classification and regression methods”, Research Day, Economics and Management Sciences Faculty, Stellenbosch University, STIAS, 24 October, 2011.

9. M.M.C. Lamont, “The nearest hypersphere classifier”, Annual conference of the South African Statistical Association, Pretoria, CSIR, 1 November, 2011.

10. N.S. Ntushelo, M.M.C. Lamont, and M. van der Rijst, “The analysis of a multidimensional binary dataset in R using the vegan package”, Annual conference of the South African Statistical Association, Pretoria, CSIR, 3 November, 2011.

11. M.M.C. Lamont, “Aspects of the v-soft minimal hypersphere as an outlier detector for multivariate data”, Annual conference of the South African Statistical Association, Port Elizabeth, NMMU, 9 November, 2012.

12. M.M.C. Lamont and H. Erasmus, “Estimating kernel parameters in support vector regression”, Annual conference of the South African Statistical Association, Limpopo, University of Limpopo, 7 November, 2013.

13. H. Erasmus and M.M.C. Lamont, “Prediction accuracy estimation with applications to classical linear regression, regression trees and support vector regression”, Annual conference of the South African Statistical Association, Limpopo, University of Limpopo, 7 November, 2013.

14. C.S. van der Westhuizen and M.M.C. Lamont, “Nearest hypersphere classification in comparison to traditional and other modern classification techniques”, Annual conference of the South African Statistical Association, Limpopo, University of Limpopo, 7 November, 2013.

15. M.M.C. Lamont, “Estimating kernel hyper-parameters in kernel Fisher discriminant analysis”, Annual conference of the South African Statistical Association, Grahamstown, Rhodes University,  29 October, 2014.

16. M.M.C. Lamont, “Sparse linear discriminant analysis for high-dimensional data: techniques and packages in R”, Annual conference of the South African Statistical Association, Bloemfontein, University of the Free State, 29 November, 2017.

17. F. Coetzer and M.M.C. Lamont, “Aspects of multi-class nearest hypersphere classification”, Annual conference of the South African Statistical Association, Bloemfontein, University of the Free State, 29 November, 2017.

18. N.S. Ntushelo, M.M.C. Lamont, and M. van der Rijst, “An exploratory analysis of multidimensional binary data using Correspondence Analysis, Non‑metric multidimensional scaling and Cluster analysis as applied to Biolog EcoPlate data”, SUSAN and SSACAB joint conference, 8-11 September, 2019.

19. P.C. Manefeldt, M.M.C. Lamont and S. Lubbe, “Extending Random Forest Biplots through Manifold Exploration”, Annual conference of the South African Statistical Association, Durban, University of Kwazulu-Natal, 27 November, 2023. 

1. Du Plessis, H.W., Steger, C.L.C., Du Toit, M. & Lambrechts, M.G., The occurrence of malolactic fermentation in brandy base wine and its influence on brandy quality, Journal of applied microbiology, 2002. URL: https://doi.org/10.1046/j.1365-2672.2002.01616.x

2. Botha, A., Denman, S., Lamprecht, S.C., Mazzola, M. & Crous, P.W., Characterisation and pathogenicity of Rhizoctonia isolates associated with black root rot of strawberries in the Western Cape Province, South Africa, Australasian Plant Pathology, 2003. URL: https://doi.org/10.1071/AP02067

3. de Beer, D., Joubert, E., Gelderbloem, W.C.A. & Manley, M., Changes in the phenolic composition and antioxidant activity of Pinotage, Cabernet Sauvignon, Chardonnay and Chenin blanc wines during bottle ageing, 2005. 

URL: https://www.cabdirect.org/cabdirect/abstract/20053128756 

4. van Jaarsveld, F.P., Hattingh, S., Marais, J. & Blom, M., Effect of Juice Turbidity and Yeast Lees Content on Brandy Base Wine and Unmatured Pot-still Brandy Quality, South African Journal of Enology and Viticulture, 2005. 

URL: https://doi.org/10.21548/26-2-2126 

5. Minnaar, P.P, Rohwer, E.R. & Booyse, M., Investigating the Use of Element Analysis for Differentiation between the Geographic Origins of Western Cape Wines, South African Journal of Enology and Viticulture, 2005. URL: https://doi.org/10.21548/26-2-2124 

6. de Beer, Dalene, The antioxidant activity of South African wines in different test systems as affected by cultivar and ageing, MSc thesis, 2001. URL: http://hdl.handle.net/10019.1/16298   

7. Rabie, Louise, An evaluation of the specific apple replant problem in Western Cape orchard soils, MSc thesis, 2001. URL: http://hdl.handle.net/10019.1/52108

8. Meyer, André, Arbuscular mycorrhizal root colonisation and the subsequent host plant response in young grapevines in a South African commercial vineyard, MSc thesis, 2005. URL: http://hdl.handle.net/10019.1/53289

9. Owen Rhode, Intraspecies diversity of Cryptococcus laurentii (Kufferath) C.E. Skinner and Cryptococcus podzolicus (Bab’eva & Reshetova) originating from a single soil sample, MSc thesis, 2005. URL: http://hdl.handle.net/10019.1/1812

10. Gardee, Muhammed, Recovery of vegetation and bees after removal of pine forests by fire in the Limietberg region of the Cape Floristic Region biodiversity hotspot, MSc thesis, 2015. URL: http://hdl.handle.net/10019.1/97796

11. Domingo, Garth, Financial literacy in the micro to small catering industry: an empirical analysis, MBA thesis, Gordon Institute of Business Science, University of Pretoria, January 2017.

12. Denga, Athenkosi, Factors influencing beef sales in South Africa, MBA thesis, Nelson Mandela University, 2019.