11 search results - page 2 / 3 » Semi-Supervised Fuzzy-Rough Feature Selection |
ICASSP
2010
IEEE
13 years 5 months ago
2010
IEEE
Supervised learning uses a training set of labeled examples to compute a classifier which is a mapping from feature vectors to class labels. The success of a learning algorithm i...
NIPS
2004
13 years 6 months ago
2004
A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple view...
BIOCOMP
2007
13 years 6 months ago
2007
The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically us...
ICML
2003
IEEE
14 years 5 months ago
2003
IEEE
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
RSKT
2010
Springer
13 years 3 months ago
2010
Springer
Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied t...