This paper describes some of the interactions of model learning algorithms and planning algorithms we have found in exploring model-based reinforcement learning. The paper focuses...
- In the general case of non-uniformly spaced frequency domain data and/or arbitrarily colored disturbing noise, the frequency domain subspace identification algorithms described i...
Five image segmentation algorithms are evaluated: mean shift, normalised cuts, efficient graph-based segmentation, hierarchical watershed, and waterfall. The evaluation is done us...
In this work we present a novel method to model instance-level constraints within a clustering algorithm. Thereby, both similarity and dissimilarity constraints can be used coeval...
This paper proposes a novel database replication algorithm that offers strong consistency (linearizable semantics) and allows reads and non-conflicting writes to execute in para...