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PAMI
2011
13 years 4 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
KDD
2006
ACM
149views Data Mining» more  KDD 2006»
14 years 9 months ago
Regularized discriminant analysis for high dimensional, low sample size data
Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining, machine learning, and bioinformatics. Friedman proposed a compromise between Linear ...
Jieping Ye, Tie Wang
GECCO
2005
Springer
134views Optimization» more  GECCO 2005»
14 years 2 months ago
Predicting mining activity with parallel genetic algorithms
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa ...
Sam Talaie, Ryan E. Leigh, Sushil J. Louis, Gary L...
CVPR
2006
IEEE
14 years 11 months ago
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
We develop theory and algorithms to incorporate image manifold constraints in a level set segmentation algorithm. This provides a framework to simultaneously segment every image o...
Qilong Zhang, Richard Souvenir, Robert Pless
ECCV
2002
Springer
14 years 11 months ago
Evaluating Image Segmentation Algorithms Using the Pareto Front
Image segmentation is the first stage of processing in many practical computer vision systems. While development of particular segmentation algorithms has attracted considerable re...
Mark Everingham, Henk L. Muller, Barry T. Thomas