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ALT
2009
Springer
15 years 10 months ago
Learning and Domain Adaptation
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, y...
Yishay Mansour
ICML
2008
IEEE
16 years 7 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
ALT
1999
Springer
15 years 11 months ago
PAC Learning with Nasty Noise
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz
AIIDE
2006
15 years 8 months ago
Predicting User Physiological Response for Interactive Environments: An Inductive Approach
Affective reasoning holds great potential for interactive digital entertainment, education, and training. Incorporating affective reasoning into the decision-making capabilities o...
Scott W. McQuiggan, Sunyoung Lee, James C. Lester
IUI
2003
ACM
15 years 12 months ago
Evolution of user interaction: the case of agent adele
Animated pedagogical agents offer promise as a means of making computer-aided learning more engaging and effective. To achieve this, an agent must be able to interact with the lea...
W. Lewis Johnson, Erin Shaw, Andrew Marshall, Cath...