In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional... Show moreIn this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations. CFD simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains and they are frequently used for studying the effects of flow pat-terns and design choices on many engineering designs, such as wing, car and engineshapes. Due to the high dimensional aspect of the data, it is difficult to model to-ward achieving critical goals such as optimizing lift and drag forces. The key research question addressed in this thesis is whether we develop automated approaches that accurately abstract this information? We tackle these issues by studying a closely re-lated field, 3D computer vision, and adapt approaches to the particular data type.Moreover, inspired by this data type we propose new, deep learning, approaches that are also applied to traditional computer vision. Show less
The central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical... Show moreThe central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical variables in regression analysis. (CATREG is implemented in SPSS Categories by the author of the thesis; the relevant parts of the Categories manual are included in the appendix.) The first chapter of the thesis provides a non-technical introduction to the CATREG approach, illustrated with graphs. The more technical part of the thesis includes (1) a solution to the local minima problem for monotone transformations, as well as a study of the effect of several data conditions on the incidence and severeness of local minima, (2) the incorporation into CATREG of a particular resampling method (the .632 bootstrap) for assessing prediction accuracy, and (3) the incorporation into CATREG of several regularization methods (Ridge Regression, the Lasso, and the Elastic Net) for stabilizing the estimates of the regression coefficients and transformations. The technical part is followed by a chapter describing a bulimia nervosa study in which the CATREG-Lasso and the .632 bootstrap are applied. Show less
