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ICML
2009
IEEE
14 years 10 months ago
Learning Markov logic network structure via hypergraph lifting
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. Learning ML...
Stanley Kok, Pedro Domingos
AIIA
2005
Springer
14 years 2 months ago
Experimental Evaluation of Hierarchical Hidden Markov Models
Building profiles for processes and for interactive users is a important task in intrusion detection. This paper presents the results obtained with a Hierarchical Hidden Markov Mo...
Attilio Giordana, Ugo Galassi, Lorenza Saitta
AE
2001
Springer
14 years 1 months ago
Markov Random Field Modelling of Royal Road Genetic Algorithms
Abstract. Markov Random Fields (MRFs) 5] are a class of probabalistic models that have been applied for many years to the analysis of visual patterns or textures. In this paper, ou...
Deryck F. Brown, A. Beatriz Garmendia-Doval, John ...
ALENEX
2003
137views Algorithms» more  ALENEX 2003»
13 years 10 months ago
The Markov Chain Simulation Method for Generating Connected Power Law Random Graphs
Graph models for real-world complex networks such as the Internet, the WWW and biological networks are necessary for analytic and simulation-based studies of network protocols, al...
Christos Gkantsidis, Milena Mihail, Ellen W. Zegur...
IJCAI
2003
13 years 10 months ago
Hierarchical Hidden Markov Models for Information Extraction
Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learni...
Marios Skounakis, Mark Craven, Soumya Ray