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NEUROSCIENCE
2001
Springer
15 years 11 months ago
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
Doina Caragea, Adrian Silvescu, Vasant Honavar
CVPR
2009
IEEE
17 years 1 months ago
Let the Kernel Figure it Out; Principled Learning of Pre-processing for Kernel Classifiers
Most modern computer vision systems for high-level tasks, such as image classification, object recognition and segmentation, are based on learning algorithms that are able to se...
Peter V. Gehler, Sebastian Nowozin
ICML
1999
IEEE
16 years 7 months ago
Implicit Imitation in Multiagent Reinforcement Learning
Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively obs...
Bob Price, Craig Boutilier
IUI
2009
ACM
16 years 3 months ago
From geek to sleek: integrating task learning tools to support end users in real-world applications
Numerous techniques exist to help users automate repetitive tasks; however, none of these methods fully support enduser creation, use, and modification of the learned tasks. We pr...
Aaron Spaulding, Jim Blythe, Will Haines, Melinda ...
KES
2005
Springer
16 years 3 hour ago
User Preference Learning for Multimedia Personalization in Pervasive Computing Environment
Pervasive computing environment and users’ demand for multimedia personalization precipitate a need for personalization tools to help people access desired multimedia content at ...
Zhiwen Yu, Daqing Zhang, Xingshe Zhou, Changde Li