Print Email Facebook Twitter Clustering Learning Curves in Machine Learning using K-Means Algorithm Title Clustering Learning Curves in Machine Learning using K-Means Algorithm: Can patterns be identified amongst learning curves after the application of the K-Means algorithm using point and statistical vectors? Author Ramsundersingh, Pravesha (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Viering, T.J. (mentor) Turan, O.T. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2024-02-01 Abstract A learning curve can serve as an indicator of the “performance of trained models versus the training set size” [1]. Recent research on learning curve analysis has been carried out within the Learning Curve Database (LCDB) [2] This paper will investigate if there are similarities amongst these curves by clustering those provided by the LCDB. The experiment employs two distinct input parameters: point vectors and statistical vectors. By conducting individual learner analysis, individual dataset analysis, principal component analysis, and other experiments, patterns are isolated for both input sets. Upon further exploration of shapes and distributions, the concluding remark is that the point vector clustering produced one key concrete pattern amongst certain learning techniques. In contrast, the statistical vector findings are more inconclusive and do not exhibit a clear distinction that could be linked to any dominant patterns. Subject Machine learningclusteringlearning curves To reference this document use: http://resolver.tudelft.nl/uuid:f196ff1d-8a37-466f-96a3-e2a6f83d6e5e Part of collection Student theses Document type bachelor thesis Rights © 2024 Pravesha Ramsundersingh Files PDF RP_Final_Draft_-_Pravesha ... rsingh.pdf 13 MB Close viewer /islandora/object/uuid:f196ff1d-8a37-466f-96a3-e2a6f83d6e5e/datastream/OBJ/view