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ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
15 years 10 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»
15 years 10 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...
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ICDM
2006
IEEE
182views Data Mining» more  ICDM 2006»
15 years 10 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»
15 years 10 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...
ICCV
2005
IEEE
15 years 10 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