BMCBI
2008
13 years 5 months ago
2008
Background: In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the cu...
SDM
2007
SIAM
13 years 6 months ago
2007
SIAM
The recent years have witnessed a surge of interest in graphbased semi-supervised learning methods. The common denominator of these methods is that the data are represented by the...
IJCAI
2007
13 years 6 months ago
2007
This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method...
ASUNAM
2010
IEEE
13 years 6 months ago
2010
IEEE
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...
AAAI
2008
13 years 7 months ago
2008
The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms is to require that the predicted data labels shoul...
CICLING
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate ...
ICPR
2008
IEEE
13 years 11 months ago
2008
IEEE
Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully specified by an Euclidean distance based local ne...
PKDD
2009
Springer
13 years 11 months ago
2009
Springer
Most existing semi-supervised learning methods are based on the smoothness assumption that data points in the same high density region should have the same label. This assumption, ...