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Tranquilizer misuse is a behavior associated with substance use disorder. As of now there is only one published article that includes a predictive model on misuse of subscription tranquilizers. The aim of this study is to predict ongoing tranquilizer misuse whilst comparing three different methods of classification; (1) regularized logistic regression, (2) adaptive boosting and (3) support vector machines. Data from the National Survey of Drug Use and Health (NSDUH) from 2019 is used to predict misuse among the individuals in the sample from 2020. The regularized logistic regression and the support vector machines models both yield an AUC of 0.88, which is slightly higher than the adaptive boosting model. However, the support vector machine model yields a higher level of sensitivity, meaning that it is better at detecting individuals who misuse. Although the difference in performance between the methods is relatively small and is most likely caused by the fact that different methods perform differently depending on the characteristics of the data.

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This thesis aims to simultaneously minimize function error and model complexity for data fusion via the Choquet integral (CI). The CI is a generator function, i.e., it is parametric and yields a wealth of aggregation operators based on the specifics of the underlying fuzzy measure. It is often the case that we desire to learn a fusion from data and the goal is to have the smallest possible sum of squared error between the trained model and a set of labels. However, we also desire to learn as “simple’’ of solutions as possible. Herein, L1-norm regularization of a lexicographically encoded capacity vector relative to the CI is explored. The impact of regularization is explored in terms of what capacities and aggregation operators it induces under different common and extreme scenarios. Synthetic experiments are provided in order to illustrate the propositions and concepts put forth.

A process for the use of multihypothesis prediction in the reconstruction of images is proposed for use in both compressed-sensing reconstruction as well as single-image super-resolution. Specifically, for compressed-sensing reconstruction of a single still image, multiple predictions for an image block are drawn from spatially surrounding blocks within an initial non-predicted reconstruction. The predictions are used to generate a residual in the domain of the compressed-sensing random projections. This residual being typically more compressible than the original signal leads to improved compressed-sensing reconstruction quality. To appropriately weight the hypothesis predictions, a Tikhonov regularization to an ill-posed least-squares optimization is proposed. An extension of this framework is applied to the compressed-sensing reconstruction of hyperspectral imagery is also studied. Finally, the multihypothesis paradigm is employed for single-image superresolution wherein each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches.

Modern neural networks are powerful predictive models. However, when it comes to recognizing that they may be wrong about their predictions and measuring the certainty of beliefs, they perform poorly. For one of the most common activation functions, the ReLU and its variants, even a well-calibrated model can produce incorrect but high confidence predictions. In the related task of action recognition, most current classification methods are based on clip-level classifiers that densely sample a given video for non-overlapping, same sized clips and aggregate the results using an aggregation function - typically averaging - to achieve video level predictions. While this approach has shown to be effective, it is sub-optimal in recognition accuracy and has a high computational overhead. To mitigate both these issues, we propose the confidence distillation framework to firstly teach a representation of uncertainty of the teacher to the student and secondly divide the task of full video prediction between the student and the teacher models. We conduct extensive experiments on three action recognition datasets and demonstrate that our framework achieves state-of-the-art results in action recognition accuracy and computational efficiency. / Thesis / Master of Science (MSc) / We devise a distillation loss function to train an efficient sampler/classifier for video-based action recognition tasks.

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