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ICML
2005
IEEE
16 years 5 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
HICSS
2008
IEEE
147views Biometrics» more  HICSS 2008»
15 years 11 months ago
Can Peer-to-Peer Networks Facilitate Information Sharing in Collaborative Learning?
Many peer-to-peer (P2P) networks have been widely used for file sharing. A peer acts both as a content provider and a consumer, and is granted autonomy to decide what content, wit...
Fu-ren Lin, Sheng-cheng Lin, Ying-fen Wang
133
Voted
ICDM
2007
IEEE
187views Data Mining» more  ICDM 2007»
15 years 11 months ago
A Comparative Study of Methods for Transductive Transfer Learning
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
Andrew Arnold, Ramesh Nallapati, William W. Cohen
ICRA
2007
IEEE
157views Robotics» more  ICRA 2007»
15 years 11 months ago
Learning to Select State Machines using Expert Advice on an Autonomous Robot
— Hierarchical state machines have proven to be a powerful tool for controlling autonomous robots due to their flexibility and modularity. For most real robot implementations, h...
Brenna Argall, Brett Browning, Manuela M. Veloso
AUSAI
2005
Springer
15 years 10 months ago
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Peter Vamplew, Robert Ollington