Abstract. Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on th...
Abstract. Robustly estimating the state-transition probabilities of highorder Markov processes is an essential task in many applications such as natural language modeling or protei...
Abstract. Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering ...
Abstract. While real-time garbage collection is now available in production virtual machines, the lack of generational capability means applications with high allocation rates are ...
Daniel Frampton, David F. Bacon, Perry Cheng, Davi...
Abstract. Classification of structured data (i.e., data that are represented as graphs) is a topic of interest in the machine learning community. This paper presents a different,...