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» Spectral Algorithms for Supervised Learning
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NIPS
2007
14 years 11 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ECCV
2008
Springer
15 years 11 months ago
SERBoost: Semi-supervised Boosting with Expectation Regularization
The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization...
Amir Saffari, Helmut Grabner, Horst Bischof
ICDM
2007
IEEE
122views Data Mining» more  ICDM 2007»
15 years 4 months ago
Noise Modeling with Associative Corruption Rules
This paper presents an active learning approach to the problem of systematic noise inference and noise elimination, specifically the inference of Associated Corruption (AC) rules...
Yan Zhang, Xindong Wu
MM
2005
ACM
146views Multimedia» more  MM 2005»
15 years 3 months ago
Unsupervised content discovery in composite audio
Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approache...
Rui Cai, Lie Lu, Alan Hanjalic
CIKM
2011
Springer
13 years 9 months ago
Exploiting longer cycles for link prediction in signed networks
We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship w...
Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari...