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ICDM
2008
IEEE
109views Data Mining» more  ICDM 2008»
15 years 10 months ago
Learning by Propagability
In this paper, we present a novel feature extraction framework, called learning by propagability. The whole learning process is driven by the philosophy that the data labels and o...
Bingbing Ni, Shuicheng Yan, Ashraf A. Kassim, Loon...
CIG
2005
IEEE
15 years 10 months ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes
IAAI
2003
15 years 5 months ago
Teachable Agents: Learning by Teaching Environments for Science Domains
The crisis in science education and the need for innovative computer-based learning environments has prompted us to develop a multi-agent system, Betty’s Brain that implements t...
Krittaya Leelawong, Karun Viswanath, Joan M. Davis...
ML
2008
ACM
156views Machine Learning» more  ML 2008»
15 years 4 months ago
On the connection between the phase transition of the covering test and the learning success rate in ILP
It is well-known that heuristic search in ILP is prone to plateau phenomena. An explanation can be given after the work of Giordana and Saitta: the ILP covering test is NP-complete...
Érick Alphonse, Aomar Osmani
TIP
2008
185views more  TIP 2008»
15 years 3 months ago
Active Learning Methods for Interactive Image Retrieval
Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensio...
Philippe Henri Gosselin, Matthieu Cord