Michael Giering

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This paper outlines a retail sales prediction and product recommendation system that was implemented for a chain of retail stores. The relative importance of consumer demographic characteristics for accurately modeling the sales of each customer type are derived and implemented in the model. Data consisted of daily sales information for 600 products at the(More)
In statistics, mixture models are used to characterize datasets with multimodal distributions. A class of mixture models called Gaussian Mixture Models (GMMs) has gained immense popularity among practitioners because of its sound statistical foundation and an efficient learning algorithm, which scales very well with both the dimension and the size of a(More)
Occlusion edges in images which correspond to range discontinuity in the scene from the point of view of the observer are an important prerequisite for many vision and mobile robot tasks. Although they can be extracted from range data however extracting them from images and videos would be extremely beneficial. We trained a deep convolutional neural network(More)
The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to(More)
We outline a method to model customer behavior from retail transaction data. In particular, we focus on the problem of recommending relevant products to consumers. Addressing this problem of filling holes in the baskets of consumers is a fundamental aspect for the success of targeted promotion programs. Another important aspect is the identification of(More)
In this work, we propose a new framework for learning mixture models from continuous data. Gaussian Mixture Models (GMMs) are commonly used for this task and are popular among practitioners because of their sound statistical foundation and the availability of an efficient learning algorithm [2]. However, the underlying assumption about the normally(More)
Recommender systems have recently gained a lot of attention both in industry and academia. In this paper, we focus on the applications and utility of recommender systems for brick-and-mortar retailers. We present a case study of a project that we completed for a large retail chain whose goal was to mine the transaction data to understand shopping behavior(More)
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