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ICML
2009
IEEE
14 years 10 months ago
Constraint relaxation in approximate linear programs
Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for...
Marek Petrik, Shlomo Zilberstein
KDD
2010
ACM
249views Data Mining» more  KDD 2010»
13 years 11 months ago
Semi-supervised sparse metric learning using alternating linearization optimization
In plenty of scenarios, data can be represented as vectors mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining app...
Wei Liu, Shiqian Ma, Dacheng Tao, Jianzhuang Liu, ...
ICML
2007
IEEE
14 years 10 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney
ML
2002
ACM
163views Machine Learning» more  ML 2002»
13 years 9 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola
HRI
2010
ACM
14 years 2 months ago
Transparent active learning for robots
—This research aims to enable robots to learn from human teachers. Motivated by human social learning, we believe that a transparent learning process can help guide the human tea...
Crystal Chao, Maya Cakmak, Andrea Lockerd Thomaz