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\section{Introduction}  The occupancy map problem learns a probabilistic model of some space. We map every point in the space within  to an estimation of  the probability that said space its  occupied. For a primer on the problem, see \cite{ramoshilbert}. Recently, a new approach for solving this problem in an effective and on-line manner was proposed. We use a logistic regression model to assign probabilities. Linear separability and model applicability is ensured through the use the kernel trick. We map the low-dimensional feature vector to a much denser space and fit a linear classification algorithm to it. Furthermore, kernel approximation methods are employed to ensure low run-time. Such a model has been demonstrated to perform very well in terms of accuracy/time tradeoff \cite{ramoshilbert}. It does however suffer from a dependence on a number of hyperparameters. Solving this problem would allow for better results and a wider application.