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ICML
2010
IEEE
13 years 10 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
CORR
2008
Springer
89views Education» more  CORR 2008»
13 years 9 months ago
Flow Faster: Efficient Decision Algorithms for Probabilistic Simulations
Strong and weak simulation relations have been proposed for Markov chains, while strong simulation and strong probabilistic simulation relations have been proposed for probabilisti...
Lijun Zhang, Holger Hermanns, Friedrich Eisenbrand...
ALDT
2009
Springer
144views Algorithms» more  ALDT 2009»
14 years 4 months ago
Aggregating Interval Orders by Propositional Optimization
Aggregating preferences for finding a consensus between several agents is an important issue in many fields, like economics, decision theory and artificial intelligence. In this...
Daniel Le Berre, Pierre Marquis, Meltem Özt&u...
NIPS
2004
13 years 11 months ago
Heuristics for Ordering Cue Search in Decision Making
Simple lexicographic decision heuristics that consider cues one at a time in a particular order and stop searching for cues as soon as a decision can be made have been shown to be...
Peter M. Todd, Anja Dieckmann
KDD
2007
ACM
177views Data Mining» more  KDD 2007»
14 years 10 months ago
Mining optimal decision trees from itemset lattices
We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal...
Élisa Fromont, Siegfried Nijssen