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ICML
2008
IEEE
16 years 7 months ago
The asymptotics of semi-supervised learning in discriminative probabilistic models
Semi-supervised learning aims at taking advantage of unlabeled data to improve the efficiency of supervised learning procedures. For discriminative models however, this is a chall...
François Yvon, Nataliya Sokolovska, Olivier...
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
16 years 18 days ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
16 years 6 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui
ICMT
2009
Springer
16 years 29 days ago
A Collection Operator for Graph Transformation
Abstract. Graph transformation has a well-established theory and associated tools that can be used to perform model transformations. However, the lack of a construct to match and t...
Roy Grønmo, Stein Krogdahl, Birger Mø...
INTERSPEECH
2010
15 years 1 months ago
Semi-supervised training of Gaussian mixture models by conditional entropy minimization
In this paper, we propose a new semi-supervised training method for Gaussian Mixture Models. We add a conditional entropy minimizer to the maximum mutual information criteria, whi...
Jui-Ting Huang, Mark Hasegawa-Johnson