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ICML
2005
IEEE
16 years 7 months ago
New d-separation identification results for learning continuous latent variable models
Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
Ricardo Silva, Richard Scheines
ICML
2003
IEEE
16 years 7 months ago
Semi-Supervised Learning of Mixture Models
This paper analyzes the performance of semisupervised learning of mixture models. We show that unlabeled data can lead to an increase in classification error even in situations wh...
Fabio Gagliardi Cozman, Ira Cohen, Marcelo Cesar C...
ICML
2003
IEEE
16 years 7 months ago
Representational Issues in Meta-Learning
To address the problem of algorithm selection for the classification task, we equip a relational case base with new similarity measures that are able to cope with multirelational ...
Alexandros Kalousis, Melanie Hilario
ICML
2000
IEEE
16 years 7 months ago
Learning to Probabilistically Identify Authoritative Documents
We describe a model of document citation that learns to identify hubs and authorities in a set of linked documents, such as pages retrieved from the world wide web, or papers retr...
David Cohn, Huan Chang
ALT
2001
Springer
16 years 3 months ago
Learning by Switching Type of Information
The present work is dedicated to the study of modes of data-presentation in the range between text and informant within the framework of inductive inference. In this study, the le...
Sanjay Jain, Frank Stephan