Machine learning with dual process models

Similarity measurement processes are a core part of most machine learning algorithms. Traditional approaches focus on either taxonomic or thematic thinking. Psychological research suggests that a combination of both is needed to model human-like similarity perception adequately. Such a combination is called a Similarity Dual Process Model (DPM). This thesis describes how to construct DPMs as a linear combination of existing measures of similarity and distance. We use generalization functions to convert distance into similarity. DPMs are similar to kernel functions. Thus, they can be integrated into any machine learning algorithm that uses kernel functions. To foster the use of DPMs, we provide kernel function implementations. Clearly, not all DPMs that can be formulated work equally well. Therefore, we test classification performance in a real-world task: the detection of pedestrians in images. We assume that DPMs are only viable if they are better classifiers than their constituting parts. In our experiments, we found DPM kernels that matched the performance of conventional kernels for our data set. Eventually, we provide a construction kit to build such kernels to encourage further experiments in other application domains of machine learning.