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NIPS
1998
15 years 1 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
97
Voted
SECURWARE
2008
IEEE
15 years 6 months ago
From Monitoring Templates to Security Monitoring and Threat Detection
Abstract. This paper presents our pattern-based approach to run-time requirements monitoring and threat detection being developed as part of an approach to build frameworks support...
Nuno Amálio, George Spanoudakis
GECCO
2004
Springer
15 years 5 months ago
Program Evolution by Integrating EDP and GP
This paper discusses the performance of a hybrid system which consists of EDP and GP. EDP, Estimation of Distribution Programming, is the program evolution method based on the prob...
Kohsuke Yanai, Hitoshi Iba
CORR
2011
Springer
174views Education» more  CORR 2011»
14 years 4 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato
94
Voted
BMCBI
2008
146views more  BMCBI 2008»
15 years 13 days ago
Rank-based edge reconstruction for scale-free genetic regulatory networks
Background: The reconstruction of genetic regulatory networks from microarray gene expression data has been a challenging task in bioinformatics. Various approaches to this proble...
Guanrao Chen, Peter Larsen, Eyad Almasri, Yang Dai