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» Some new directions in graph-based semi-supervised learning
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69
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JMLR
2010
191views more  JMLR 2010»
14 years 5 months ago
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some...
Michael Gutmann, Aapo Hyvärinen
92
Voted
ICML
2004
IEEE
15 years 11 months ago
Boosting margin based distance functions for clustering
The performance of graph based clustering methods critically depends on the quality of the distance function, used to compute similarities between pairs of neighboring nodes. In t...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
81
Voted
AIED
2009
Springer
15 years 4 months ago
Intelligent Support for Inquiry Learning from Images: A Learning Scenario and Tool
Inquiry learning involves the learner acquiring new concepts and skills by means of carrying out an investigation. Some previous studies have looked into how these learning activit...
Paul Mulholland, Zdenek Zdráhal, Jan Abraha...
87
Voted
ICANN
2003
Springer
15 years 3 months ago
The Acquisition of New Categories through Grounded Symbols: An Extended Connectionist Model
Abstract. Solutions to the symbol grounding problem, in psychologically plausible cognitive models, have been based on hybrid connectionist/symbolic architectures, on robotic appro...
Alberto Greco, Thomas Riga, Angelo Cangelosi
94
Voted
ICML
2007
IEEE
15 years 11 months ago
Bayesian compressive sensing and projection optimization
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal o...
Shihao Ji, Lawrence Carin