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107
Voted
ICML
2007
IEEE
16 years 3 months ago
Parameter learning for relational Bayesian networks
We present a method for parameter learning in relational Bayesian networks (RBNs). Our approach consists of compiling the RBN model into a computation graph for the likelihood fun...
Manfred Jaeger
114
Voted
APSEC
2007
IEEE
15 years 6 months ago
Deriving Software Process Simulation Model from SPEM-based Software Process Model
It is difficult to adopt a simulation technology for simulating a software process because of the difficulty in developing a simulation model. In order to resolve the difficulty, ...
Seunghun Park, KeungSik Choi, Kyung-A Yoon, Doo-Hw...
109
Voted
ICDM
2009
IEEE
124views Data Mining» more  ICDM 2009»
15 years 9 months ago
Rule Ensembles for Multi-target Regression
—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. ...
Timo Aho, Bernard Zenko, Saso Dzeroski
CORR
2011
Springer
136views Education» more  CORR 2011»
14 years 6 months ago
Eliciting Forecasts from Self-interested Experts: Scoring Rules for Decision Makers
Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., sco...
Craig Boutilier
99
Voted
CDC
2008
IEEE
142views Control Systems» more  CDC 2008»
15 years 9 months ago
Convergence of rule-of-thumb learning rules in social networks
— We study the problem of dynamic learning by a social network of agents. Each agent receives a signal about an underlying state and communicates with a subset of agents (his nei...
Daron Acemoglu, Angelia Nedic, Asuman E. Ozdaglar