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AAAI
2011
12 years 9 months ago
Pushing the Power of Stochastic Greedy Ordering Schemes for Inference in Graphical Models
We study iterative randomized greedy algorithms for generating (elimination) orderings with small induced width and state space size - two parameters known to bound the complexity...
Kalev Kask, Andrew Gelfand, Lars Otten, Rina Decht...
RSEISP
2007
Springer
14 years 3 months ago
Interpreting Low and High Order Rules: A Granular Computing Approach
The main objective of this paper is to provide a granular computing based interpretation of rules representing two levels of knowledge. This is done by adopting and adapting the de...
Yiyu Yao, Bing Zhou, Yaohua Chen
AAAI
1998
13 years 11 months ago
Generalizing Partial Order and Dynamic Backtracking
RecentlyFtwo new backtracking algorithmsF dynamic backtracking (DB)and partial order dynamicbacktracking (PDB) have been presented. These algorithms have the property to be additi...
Christian Bliek
ILP
2004
Springer
14 years 3 months ago
First Order Random Forests with Complex Aggregates
Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well...
Celine Vens, Anneleen Van Assche, Hendrik Blockeel...
FOIKS
2008
Springer
13 years 11 months ago
Merging First-Order Knowledge Using Dilation Operators
The area of knowledge merging is concerned with merging conflicting information while preserving as much as possible. Most proposals in the literature work with knowledge bases exp...
Nikos Gorogiannis, Anthony Hunter