This report is an expanded version of a paper in AAAI-2006 proceedings. In this report, we investigate the challenges that must be addressed for the Semantic Web to become a feasi...
- In this paper we propose a new methodology for Cost-Benefit analysis in a multiple time series prediction problem. The proposed model is evaluated in a real world application bas...
Similarity search in time series databases is an important research direction. Several methods have been proposed in order to provide algorithms for efficient query processing in t...
Maria Kontaki, Apostolos Papadopoulos, Yannis Mano...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of training samples is small and the number of predictor...