Abstract

The thesis describes the integration of the geometry aware sparse grids into thedatamining
pipeline of the SG++ framework. The datamining pipeline is used tomake SG++ easier available and fasten the process of using the framework. Geometryaware sparse grids have a great use in image classification and therefore this thesis isadditionally about creating a mobile application that classifies hand drawn numbersusing this kind of sparse grids. For this to be done the trained data had to be exportedfrom SG++ and then imported again into the application. Also the evaluation method for sparse grids was implemented into the application. This new evaluation method isthen validated by comparing it to the evaluation method of SG++. The outcome of thevalidation resulted in a high deviation for random datasets and very low deviation forrelevant datasets. The high deviation for random datasets can be neglected, since it hasno use case in the user application. Finally the application was tested for numbers 0, 2and 6 with various relevant datasets to calculate the accuracy of the implementation.The tests have shown that by following certain rules while drawing the numbers intothe application an average accuracy of 90% could be observed.

Outcome

The project was presented during the annual open house @TUM. It represents the results of the SG++ machine learning approach developed at the scientific chair.

For further information please refer to the paper linked above.