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» Learning with Kernels and Logical Representations
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ECAI
2010
Springer
13 years 8 months ago
Adaptive Markov Logic Networks: Learning Statistical Relational Models with Dynamic Parameters
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Dominik Jain, Andreas Barthels, Michael Beetz
SMC
2007
IEEE
105views Control Systems» more  SMC 2007»
14 years 5 months ago
A software architecture for adaptive modular sensing systems
Abstract—In this paper, a software architecture and knowledge representation scheme that enables the combination and reconfiguration of modular sensor and actuator components is...
Andrew C. Lyle, Michael D. Naish
AAAI
2011
12 years 11 months ago
Transfer Learning by Structural Analogy
Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critica...
Hua-Yan Wang, Qiang Yang
SSPR
2010
Springer
13 years 9 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
ECML
2006
Springer
14 years 2 months ago
TildeCRF: Conditional Random Fields for Logical Sequences
Abstract. Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat al...
Bernd Gutmann, Kristian Kersting