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PKDD
2009
Springer
181views Data Mining» more  PKDD 2009»
14 years 4 months ago
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
Manuel Lopes, Francisco S. Melo, Luis Montesano
ICDM
2005
IEEE
128views Data Mining» more  ICDM 2005»
14 years 3 months ago
An Expected Utility Approach to Active Feature-Value Acquisition
In many classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is of...
Prem Melville, Foster J. Provost, Raymond J. Moone...
ICDM
2003
IEEE
143views Data Mining» more  ICDM 2003»
14 years 3 months ago
Active Sampling for Feature Selection
In knowledge discovery applications, where new features are to be added, an acquisition policy can help select the features to be acquired based on their relevance and the cost of...
Sriharsha Veeramachaneni, Paolo Avesani
GIS
2006
ACM
14 years 10 months ago
ST-ACTS: a spatio-temporal activity simulator
Creating complex spatio?temporal simulation models is a hot issue in the area of spatio?temporal databases [7]. While existing Moving Object Simulators (MOSs) address different ph...
Gyözö Gidófalvi, Torben Bach Pede...
ICDM
2007
IEEE
254views Data Mining» more  ICDM 2007»
14 years 4 months ago
Sampling for Sequential Pattern Mining: From Static Databases to Data Streams
Sequential pattern mining is an active field in the domain of knowledge discovery. Recently, with the constant progress in hardware technologies, real-world databases tend to gro...
Chedy Raïssi, Pascal Poncelet