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» Learning Models for Predicting Recognition Performance
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137
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KDD
2008
ACM
259views Data Mining» more  KDD 2008»
16 years 3 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, ...
173
Voted
CVPR
2012
IEEE
13 years 5 months ago
Boosting bottom-up and top-down visual features for saliency estimation
Despite significant recent progress, the best available visual saliency models still lag behind human performance in predicting eye fixations in free-viewing of natural scenes. ...
Ali Borji
126
Voted
IUI
2009
ACM
15 years 11 months ago
Learning to generalize for complex selection tasks
Selection tasks are common in modern computer interfaces: we are often required to select a set of files, emails, data entries, and the like. File and data browsers have sorting a...
Alan Ritter, Sumit Basu
126
Voted
BMCBI
2010
147views more  BMCBI 2010»
15 years 2 months ago
Learning biological network using mutual information and conditional independence
Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a revers...
Dong-Chul Kim, Xiaoyu Wang, Chin-Rang Yang, Jean G...
159
Voted
DICTA
2003
15 years 4 months ago
Learning Semantic Concepts from Visual Data Using Neural Networks
For content-based image retrieval techniques, query image is used to pick up and rank some relevant images from a database using some certain similarity metric. If semantic feature...
Xiaohang Ma, Dianhui Wang