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NIPS
2008
14 years 11 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
80
Voted
FLAIRS
2004
14 years 11 months ago
The Optimality of Naive Bayes
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surpris...
Harry Zhang
85
Voted
CORR
2010
Springer
152views Education» more  CORR 2010»
14 years 10 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
80
Voted
IJWBC
2010
126views more  IJWBC 2010»
14 years 8 months ago
Self-supportive virtual communities
Learning Communities in healthcare is one approach in supporting practitioners and nurses to improve skills and knowledge and consequently support patients more efficiently. Web b...
Iraklis Varlamis, Ioannis Apostolakis
ECCV
2006
Springer
16 years 2 days ago
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation
Abstract. This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned ...
Jamie Shotton, John M. Winn, Carsten Rother, Anton...