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ML
2008
ACM
150views Machine Learning» more  ML 2008»
13 years 9 months ago
Learning probabilistic logic models from probabilistic examples
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Jianzhong Chen, Stephen Muggleton, José Car...
ICPR
2010
IEEE
13 years 8 months ago
Learning Non-Linear Dynamical Systems by Alignment of Local Linear Models
Abstract—Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works...
Masao Joko, Yoshinobu Kawahara, Takehisa Yairi
CCIA
2010
Springer
13 years 4 months ago
Learning Force-Based Robot Skills from Haptic Demonstration
Locally weighted as well as Gaussian mixtures learning algorithms are suitable strategies for trajectory learning and skill acquisition, in the context of programming by demonstrat...
Leonel Rozo, Pablo Jiménez, Carme Torras
ICML
2008
IEEE
14 years 10 months ago
Adaptive p-posterior mixture-model kernels for multiple instance learning
In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mech...
Hua-Yan Wang, Qiang Yang, Hongbin Zha
ICRA
2003
IEEE
222views Robotics» more  ICRA 2003»
14 years 2 months ago
Path planning using learned constraints and preferences
— In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of ...
Gregory Dudek, Saul Simhon