Abstract This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that r...
Roman Katz, Juan Nieto, Eduardo Mario Nebot, Bertr...
An operational framework is developed for testing stationarity relatively to an observation scale, in both stochastic and deterministic contexts. The proposed method is based on a ...
Pierre Borgnat, Patrick Flandrin, Paul Honeine, C&...
We present a tool that predicts whether the software under development inside an IDE has a bug. An IDE plugin performs this prediction, using the Change Classification technique t...
Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination ...
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade fi...
Andrej Bratko, Gordon V. Cormack, Bogdan Filipic, ...