230 search results - page 3 / 46 » Semisupervised learning from dissimilarity data |
NIPS
2007
13 years 8 months ago
2007
Semi-supervised methods use unlabeled data in addition to labeled data to construct predictors. While existing semi-supervised methods have shown some promising empirical performa...
ICASSP
2010
IEEE
13 years 7 months ago
2010
IEEE
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
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Voted
KDD
2004
ACM
14 years 7 months ago
2004
ACM
Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure betwee...
NIPS
2007
13 years 8 months ago
2007
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...
ICCV
2007
IEEE
14 years 1 months ago
2007
IEEE
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...