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FLAIRS
2008
13 years 12 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
ECML
2007
Springer
14 years 3 months ago
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendr...
SBIA
2000
Springer
14 years 1 months ago
User profiling with Case-Based Reasoning and Bayesian Networks
Agent technology provides many services to users. The tasks in which agents are involved include information filtering, information retrieval, user's tasks automation, browsin...
Silvia N. Schiaffino, Analía Amandi
ICRA
2007
IEEE
132views Robotics» more  ICRA 2007»
14 years 4 months ago
Heterogeneous Leg Stiffness and Roll in Dynamic Running
— Legged robots are by nature strongly non-linear, high-dimensional systems whose full complexity permits neither tractable mathematical analysis nor comprehensive numerical stud...
Samuel Burden, Jonathan Clark, Joel Weingarten, Ha...
NIPS
2000
13 years 11 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick