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» Experimental perspectives on learning from imbalanced data
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ICCV
2011
IEEE
13 years 9 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
CHI
2010
ACM
15 years 4 months ago
Lessons learned from blog muse: audience-based inspiration for bloggers
Blogging in the enterprise is increasingly popular and recent research has shown that there are numerous benefits for both individuals and the organization, e.g. developing reputa...
Casey Dugan, Werner Geyer, David R. Millen
CVPR
2005
IEEE
15 years 11 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu
IJCAI
1997
14 years 11 months ago
Using Case-Based Reasoning in Interpreting Unsupervised Inductive Learning Results
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts ...
Tu Bao Ho, Chi Main Luong
RSFDGRC
2005
Springer
190views Data Mining» more  RSFDGRC 2005»
15 years 3 months ago
Finding Rough Set Reducts with SAT
Abstract. Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine...
Richard Jensen, Qiang Shen, Andrew Tuson