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ATAL
2010
Springer
15 years 7 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
ASC
2008
15 years 6 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
BMCBI
2006
112views more  BMCBI 2006»
15 years 6 months ago
Protein disorder prediction by condensed PSSM considering propensity for order or disorder
Background: More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that dis...
Chung-Tsai Su, Chien-Yu Chen, Yu-Yen Ou
JAIR
2006
157views more  JAIR 2006»
15 years 6 months ago
Decision-Theoretic Planning with non-Markovian Rewards
A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic...
Sylvie Thiébaux, Charles Gretton, John K. S...
SIGKDD
2008
125views more  SIGKDD 2008»
15 years 6 months ago
Incremental pattern discovery on streams, graphs and tensors
Incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. The questions are how to find patterns (main trends) incrementally; ...
Jimeng Sun