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» Bayesian Inference for Sparse Generalized Linear Models
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KDD
2008
ACM
259views Data Mining» more  KDD 2008»
16 years 2 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, ...
CIKM
2009
Springer
15 years 8 months ago
Scalable learning of collective behavior based on sparse social dimensions
The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr a...
Lei Tang, Huan Liu
CIKM
2008
Springer
15 years 3 months ago
A sparse gaussian processes classification framework for fast tag suggestions
Tagged data is rapidly becoming more available on the World Wide Web. Web sites which populate tagging services offer a good way for Internet users to share their knowledge. An in...
Yang Song, Lu Zhang 0007, C. Lee Giles
GECCO
2006
Springer
220views Optimization» more  GECCO 2006»
15 years 5 months ago
Comparing evolutionary algorithms on the problem of network inference
In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different evoluti...
Christian Spieth, Rene Worzischek, Felix Streicher...
129
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ICCV
2009
IEEE
16 years 6 months ago
A Global Perspective on MAP Inference for Low-Level Vision
In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, ...
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo...