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ICML
1997
IEEE
16 years 7 months ago
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scen...
Nir Friedman
ANNPR
2008
Springer
15 years 8 months ago
Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network
Abstract. Automatic pattern classifiers that allow for on-line incremental learning can adapt internal class models efficiently in response to new information without retraining fr...
Jean-François Connolly, Eric Granger, Rober...
TLT
2008
93views more  TLT 2008»
15 years 6 months ago
What Do You Prefer? Using Preferences to Enhance Learning Technology
While the growing number of learning resources increases the choice for learners on how, what and when to learn, it also makes it more and more difficult to find the learning resou...
Philipp Kärger, Daniel Olmedilla, Fabian Abel...
NECO
1998
168views more  NECO 1998»
15 years 6 months ago
Constructive Incremental Learning from Only Local Information
We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, t...
Stefan Schaal, Christopher G. Atkeson
BMCBI
2010
150views more  BMCBI 2010»
15 years 6 months ago
Automatic structure classification of small proteins using random forest
Background: Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the simi...
Pooja Jain, Jonathan D. Hirst