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» Learning Probabilistic Models of Relational Structure
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COLT
1994
Springer
15 years 4 months ago
Bayesian Inductive Logic Programming
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learnin...
Stephen Muggleton
IJCAI
2003
15 years 1 months ago
Spaces of Theories with Ideal Refinement Operators
Refinement operators for theories avoid the problems related to the myopia of many relational learning algorithms based on the operators that refine single clauses. However, the n...
Nicola Fanizzi, Stefano Ferilli, Nicola Di Mauro, ...
ADBIS
1995
Springer
155views Database» more  ADBIS 1995»
15 years 4 months ago
The MaStA I/O Cost Model and its Validation Strategy
Crash recovery in database systems aims to provide an acceptable level of protection from failure at a given engineering cost. A large number of recovery mechanisms are known, and...
S. Scheuerl, Richard C. H. Connor, Ronald Morrison...
96
Voted
ICMI
2010
Springer
129views Biometrics» more  ICMI 2010»
14 years 10 months ago
Quantifying group problem solving with stochastic analysis
Quantifying the relationship between group dynamics and group performance is a key issue of increasing group performance. In this paper, we will discuss how group performance is r...
Wen Dong, Alex Pentland
111
Voted
BMCBI
2007
138views more  BMCBI 2007»
15 years 15 days ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...