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ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
16 years 1 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
ICDM
2006
IEEE
129views Data Mining» more  ICDM 2006»
16 years 1 months ago
Getting the Most Out of Ensemble Selection
We investigate four previously unexplored aspects of ensemble selection, a procedure for building ensembles of classifiers. First we test whether adjusting model predictions to p...
Rich Caruana, Art Munson, Alexandru Niculescu-Mizi...
204
Voted
ICDM
2006
IEEE
182views Data Mining» more  ICDM 2006»
16 years 1 months ago
Active Learning to Maximize Area Under the ROC Curve
In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. ...
Matt Culver, Kun Deng, Stephen D. Scott
ADMA
2006
Springer
172views Data Mining» more  ADMA 2006»
16 years 1 months ago
Experimental Comparison of Feature Subset Selection Using GA and ACO Algorithm
Abstract. Practical pattern classification and knowledge discovery problems require selecting a useful subset of features from a much larger set to represent the patterns to be cl...
Keunjoon Lee, Jinu Joo, Jihoon Yang, Vasant Honava...
209
Voted
ICCV
2005
IEEE
16 years 1 months ago
Common Pattern Discovery Using Earth Mover's Distance and Local Flow Maximization
In this paper, we present a novel segmentationinsensitive approach for mining common patterns from 2 images. We develop an algorithm using the Earth Movers Distance (EMD) framewor...
Hung-Khoon Tan, Chong-Wah Ngo