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» Using Learning for Approximation in Stochastic Processes
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AIED
2011
Springer
12 years 11 months ago
Faster Teaching by POMDP Planning
Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling stu...
Anna N. Rafferty, Emma Brunskill, Thomas L. Griffi...
ICML
2008
IEEE
14 years 8 months ago
An HDP-HMM for systems with state persistence
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
AAAI
1998
13 years 8 months ago
Iterated Phantom Induction: A Little Knowledge Can Go a Long Way
Weadvance a knowledge-based learning method that augments conventional generalization to permit concept acquisition in failure domains. These are domains in whichlearning must pro...
Mark Brodie, Gerald DeJong
CVPR
2003
IEEE
14 years 9 months ago
Video-Based Face Recognition Using Probabilistic Appearance Manifolds
This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the amb...
Kuang-Chih Lee, Jeffrey Ho, Ming-Hsuan Yang, David...
CVPR
2009
IEEE
15 years 2 months ago
Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies
This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We dea...
Richard Roberts (Georgia Institute of Technology),...