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AROBOTS
2004
99views more  AROBOTS 2004»
13 years 7 months ago
Bayesian Robot Programming
We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are d...
Olivier Lebeltel, Pierre Bessière, Julien D...
FLAIRS
2006
13 years 9 months ago
Focusing AI Students' Attention: A Framework-Based Approach to Guiding Impasse-Driven Learning
Research indicates that impasse-driven learning can have important benefits for improving student mastery of material. When students recognize gaps in their understanding of a con...
Steven Bogaerts, David B. Leake
VL
2010
IEEE
160views Visual Languages» more  VL 2010»
13 years 6 months ago
Toward End-User Debugging of Machine-Learned Classifiers
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a generat...
Todd Kulesza
ML
1998
ACM
117views Machine Learning» more  ML 1998»
13 years 7 months ago
Learning Team Strategies: Soccer Case Studies
We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy, but may behave di erently due to position-dependent inputs. All...
Rafal Salustowicz, Marco Wiering, Jürgen Schm...
IJCNN
2006
IEEE
14 years 1 months ago
Learning the Kernel in Mahalanobis One-Class Support Vector Machines
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
Ivor W. Tsang, James T. Kwok, Shutao Li