Machine learning under resource constraints Katharina Morik, Peter Marwedel (Eds.) Machine
Learning under Resource Constraints· Fundamentals Page 2 Also of interest Volume 2 …

Data summarization has become a valuable tool in understanding even terabytes of data.
Due to their compelling theoretical properties, submodular functions have been the focus of …

Machine Learning under Resource Constraints addresses novel machine learning
algorithms that are challenged by high-throughput data, by high dimensions, or by complex …

Traffic congestion is one of the most pressing issues for smart cities. Information on traffic
flow can be used to reduce congestion by predicting vehicle counts at unmonitored locations …

In this work, we consider the problem of summarizing a data stream through an item-based
summary using core items. We consider an IoT setting, where computing such summaries at …

The penetration of wireless communication systems in industrial and private environments is
constantly increasing due to their flexible and mobile application possibilities. Wearables …

With increasing volumes in data and more sophisticated machine learning algorithms, the
demand for fast and energy efficient computation systems is also growing. To meet this …