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MCS
2007
Springer
14 years 1 months ago
Selecting Diversifying Heuristics for Cluster Ensembles
Abstract. Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ...
Stefan Todorov Hadjitodorov, Ludmila I. Kuncheva
CORR
2010
Springer
117views Education» more  CORR 2010»
13 years 8 months ago
Evolution with Drifting Targets
We consider the question of the stability of evolutionary algorithms to gradual changes, or drift, in the target concept. We define an algorithm to be resistant to drift if, for s...
Varun Kanade, Leslie G. Valiant, Jennifer Wortman ...
ICML
2007
IEEE
14 years 8 months ago
Learning a meta-level prior for feature relevance from multiple related tasks
In many prediction tasks, selecting relevant features is essential for achieving good generalization performance. Most feature selection algorithms consider all features to be a p...
Su-In Lee, Vassil Chatalbashev, David Vickrey, Dap...
CVPR
2007
IEEE
14 years 9 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
VIP
2003
13 years 9 months ago
Optimal Selection of Image Segmentation Algorithms Based on Performance Prediction
Using different algorithms to segment different images is a quite straightforward strategy for automated image segmentation. But the difficulty of the optimal algorithm selection ...
Yong Xia, David Dagan Feng, Rongchun Zhao