7 citations · 8 across the 2 of their papers we have counts for
4 papers · 1 filter
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
Marco Heyden, Edouard Fouché, Vadim Arzamasov +3
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., b…
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
Marco Heyden, Vadim Arzamasov, Edouard Fouché +1
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from arms with unknown expected rewards and costs. The goal is to maximize the total r…
Efficient Subspace Search in Data Streams
Edouard Fouché, Florian Kalinke, Klemens Böhm
In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time.…
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary
Adrian Englhardt, Holger Trittenbach, Daniel Kottke +2
Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size.…