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IJCAI
2003
13 years 11 months ago
When Discriminative Learning of Bayesian Network Parameters Is Easy
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Hannes Wettig, Peter Grünwald, Teemu Roos, Pe...
JUCS
2008
166views more  JUCS 2008»
13 years 10 months ago
Learning Design in Adaptive Educational Hypermedia Systems
: After more than ten years of research aiming at defining methods and techniques to deliver personalized instruction, Adaptive Educational Hypermedia Systems have not made the jum...
Adriana J. Berlanga, Francisco José Garc&ia...
NN
2006
Springer
153views Neural Networks» more  NN 2006»
13 years 10 months ago
An incremental network for on-line unsupervised classification and topology learning
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion c...
Shen Furao, Osamu Hasegawa
ML
2000
ACM
126views Machine Learning» more  ML 2000»
13 years 10 months ago
Learning to Play Chess Using Temporal Differences
In this paper we present TDLEAF( ), a variation on the TD( ) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our che...
Jonathan Baxter, Andrew Tridgell, Lex Weaver
EMNLP
2009
13 years 8 months ago
Semi-Supervised Learning for Semantic Relation Classification using Stratified Sampling Strategy
This paper presents a new approach to selecting the initial seed set using stratified sampling strategy in bootstrapping-based semi-supervised learning for semantic relation class...
Longhua Qian, Guodong Zhou, Fang Kong, Qiaoming Zh...