Probabilistic verification of continuous-time stochastic processes has received increasing attention in the model-checking community in the past five years, with a clear focus on ...
We investigate the computational complexity of the task of detecting dense regions of an unknown distribution from un-labeled samples of this distribution. We introduce a formal l...
We present the results of a systematic study of the contextual gain hypothesis for image classification. This hypothesis relates the traditional strategy of direct visual classi...
The graph classification problem is learning to classify separate, individual graphs in a graph database into two or more categories. A number of algorithms have been introduced fo...
Nikhil S. Ketkar, Lawrence B. Holder, Diane J. Coo...
We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube...