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ICML
2004
IEEE
16 years 5 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
ICML
2003
IEEE
16 years 5 months ago
Learning with Knowledge from Multiple Experts
The use of domain knowledge in a learner can greatly improve the models it produces. However, high-quality expert knowledge is very difficult to obtain. Traditionally, researchers...
Matthew Richardson, Pedro Domingos
157
Voted
PKDD
2009
Springer
170views Data Mining» more  PKDD 2009»
15 years 11 months ago
Statistical Relational Learning with Formal Ontologies
Abstract. We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation...
Achim Rettinger, Matthias Nickles, Volker Tresp
IJCNN
2007
IEEE
15 years 11 months ago
Incorporating Forgetting in a Category Learning Model
— We present a computational model of human category learning that learns the essential structures of the categories by forgetting information that is not useful for the given ta...
Yasuaki Sakamoto, Toshihiko Matsuka
163
Voted
ICMCS
2005
IEEE
126views Multimedia» more  ICMCS 2005»
15 years 10 months ago
A HMM-Embedded Unsupervised Learning to Musical Event Detection
In this paper, an HMM-embedded unsupervised learning approach is proposed to detect the music events by grouping the similar segments of the music signal. This approach can cluste...
Sheng Gao, Yongwei Zhu