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JMLR
2006
186views more  JMLR 2006»
13 years 10 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
KDD
2008
ACM
259views Data Mining» more  KDD 2008»
14 years 11 months ago
Using ghost edges for classification in sparsely labeled networks
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
KDD
2003
ACM
157views Data Mining» more  KDD 2003»
14 years 11 months ago
Cross-training: learning probabilistic mappings between topics
Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign l...
Sunita Sarawagi, Soumen Chakrabarti, Shantanu Godb...
CVPR
2007
IEEE
15 years 23 days ago
Learning Color Names from Real-World Images
Within a computer vision context color naming is the action of assigning linguistic color labels to image pixels. In general, research on color naming applies the following paradi...
Joost van de Weijer, Cordelia Schmid, Jakob J. Ver...
DIS
2010
Springer
13 years 9 months ago
ESTATE: Strategy for Exploring Labeled Spatial Datasets Using Association Analysis
Abstract. We propose an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. Proposed strategy, EST...
Tomasz F. Stepinski, Josue Salazar, Wei Ding 0003,...