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» Unsupervised feature selection using a neuro-fuzzy approach
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EMNLP
2009
14 years 9 months ago
Classifier Combination for Contextual Idiom Detection Without Labelled Data
We propose a novel unsupervised approach for distinguishing literal and non-literal use of idiomatic expressions. Our model combines an unsupervised and a supervised classifier. T...
Linlin Li, Caroline Sporleder
137
Voted
CVPR
2006
IEEE
16 years 1 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell
JMLR
2010
108views more  JMLR 2010»
14 years 6 months ago
Feature Selection using Multiple Streams
Feature selection for supervised learning can be greatly improved by making use of the fact that features often come in classes. For example, in gene expression data, the genes wh...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
ICDM
2010
IEEE
193views Data Mining» more  ICDM 2010»
14 years 9 months ago
Supervised Link Prediction Using Multiple Sources
Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this pro...
Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit S....
ASWC
2009
Springer
15 years 4 months ago
Semantic-Linguistic Feature Vectors for Search: Unsupervised Construction and Experimental Validation
Abstract. In this paper, we elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semant...
Stein L. Tomassen, Darijus Strasunskas