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» On the Generalization Ability of On-Line Learning Algorithms
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ACMACE
2006
ACM
15 years 3 months ago
Motivated reinforcement learning for non-player characters in persistent computer game worlds
Massively multiplayer online computer games are played in complex, persistent virtual worlds. Over time, the landscape of these worlds evolves and changes as players create and pe...
Kathryn Elizabeth Merrick, Mary Lou Maher
76
Voted
SIGCSE
2004
ACM
101views Education» more  SIGCSE 2004»
15 years 3 months ago
Effective features of algorithm visualizations
Many algorithm visualizations have been created, but little is known about which features are most important to their success. We believe that pedagogically useful visualizations ...
Purvi Saraiya, Clifford A. Shaffer, D. Scott McCri...
BMCBI
2006
119views more  BMCBI 2006»
14 years 9 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
15 years 3 months ago
DXCS: an XCS system for distributed data mining
XCS is a flexible system for data mining due to its ability to deal with environmental changes, learn online with little prior knowledge and evolve accurate and maximally general...
Hai Huong Dam, Hussein A. Abbass, Chris Lokan
ICML
1995
IEEE
15 years 10 months ago
Stable Function Approximation in Dynamic Programming
The success ofreinforcement learninginpractical problems depends on the ability to combine function approximation with temporal di erence methods such as value iteration. Experime...
Geoffrey J. Gordon