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» Learning Relational Sum-Product Networks
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95
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SEAL
1998
Springer
15 years 4 months ago
Co-evolution, Determinism and Robustness
Abstract. Robustness has long been recognised as a critical issue for coevolutionary learning. It has been achieved in a number of cases, though usually in domains which involve so...
Alan D. Blair, Elizabeth Sklar, Pablo Funes
109
Voted
ESANN
2001
15 years 1 months ago
Learning fault-tolerance in Radial Basis Function Networks
This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
Xavier Parra, Andreu Català
AAAI
2011
14 years 15 days ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
102
Voted
CIBCB
2008
IEEE
15 years 7 months ago
Temporal and structural analysis of biological networks in combination with microarray data
— We introduce a graph-based relational learning approach using graph-rewriting rules for temporal and structural analysis of biological networks changing over time. The analysis...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
91
Voted
ICML
2010
IEEE
15 years 1 months ago
Learning Markov Logic Networks Using Structural Motifs
Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (4-5 literals) due to extre...
Stanley Kok, Pedro Domingos