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BMCBI
2008
99views more  BMCBI 2008»
9 years 2 days ago
Binning sequences using very sparse labels within a metagenome
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...
Chon-Kit Kenneth Chan, Arthur L. Hsu, Saman K. Hal...
SDM
2007
SIAM
104views Data Mining» more  SDM 2007»
9 years 1 months ago
Fast Multilevel Transduction on Graphs
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...
Fei Wang, Changshui Zhang
IJCAI
2007
9 years 1 months ago
Graph-Based Semi-Supervised Learning as a Generative Model
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...
Jingrui He, Jaime G. Carbonell, Yan Liu 0002
ASUNAM
2010
IEEE
9 years 1 months ago
Semi-Supervised Classification of Network Data Using Very Few Labels
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...
Frank Lin, William W. Cohen
AAAI
2008
9 years 2 months ago
On Discriminative Semi-Supervised Classification
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...
Fei Wang, Changshui Zhang
CICLING
2006
Springer
9 years 3 months ago
Application of Semi-supervised Learning to Evaluative Expression Classification
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 ...
Yasuhiro Suzuki, Hiroya Takamura, Manabu Okumura
ICPR
2008
IEEE
9 years 6 months ago
Semi-supervised learning by locally linear embedding in kernel space
Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully speciļ¬ed by an Euclidean distance based local ne...
Rujie Liu, Yuehong Wang, Takayuki Baba, Daiki Masu...
PKDD
2009
Springer
153views Data Mining» more  PKDD 2009»
9 years 6 months ago
Subspace Regularization: A New Semi-supervised Learning Method
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, ...
Yan-Ming Zhang, Xinwen Hou, Shiming Xiang, Cheng-L...
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