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ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
HEURISTICS
2008
92views more  HEURISTICS 2008»
13 years 7 months ago
Learning heuristics for basic block instruction scheduling
Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to ...
Abid M. Malik, Tyrel Russell, Michael Chase, Peter...
SPE
2002
105views more  SPE 2002»
13 years 7 months ago
Specifying a role-based guide for learning to work with an enterprise framework
Learning to work with enterprise frameworks requires considerable effort, because of the inherent complexity of all the knowledge that is needed. However, different roles in proje...
Wilhelm Hasselbring, Ralph van den Houdt
ILP
2004
Springer
14 years 27 days ago
Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction
Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. Our research has focused on Information Extraction (IE), a task that typically invol...
Mark Goadrich, Louis Oliphant, Jude W. Shavlik
ECAI
2008
Springer
13 years 9 months ago
Learning to Select Object Recognition Methods for Autonomous Mobile Robots
Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and...
Reinaldo A. C. Bianchi, Arnau Ramisa, Ramon L&oacu...