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» Regularized Learning with Networks of Features
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JMLR
2012
13 years 2 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
CORR
2010
Springer
104views Education» more  CORR 2010»
14 years 12 months ago
Empirical learning aided by weak domain knowledge in the form of feature importance
Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowle...
Ridwan Al Iqbal
CVPR
2004
IEEE
16 years 1 months ago
High-Zoom Video Hallucination by Exploiting Spatio-Temporal Regularities
In this paper, we consider the problem of super-resolving a human face video by a very high (?16) zoom factor. Inspired by recent literature on hallucination and examplebased lear...
Göksel Dedeoglu, Jonas August, Takeo Kanade
ICMLA
2010
14 years 9 months ago
Heuristic Method for Discriminative Structure Learning of Markov Logic Networks
Markov Logic Networks (MLNs) combine Markov Networks and first-order logic by attaching weights to firstorder formulas and viewing them as templates for features of Markov Networks...
Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrai...
COLING
1996
15 years 1 months ago
FeasPar - A Feature Structure Parser Learning to Parse Spoken Language
We describe and experimentally evaluate a system, FeasPar, that learns parsing spontaneous speech. To train and run FeasPar (Feature Structure Parser), only limited handmodeled kn...
Finn Dag Buø, Alex Waibel