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» Reduction Techniques for Instance-Based Learning Algorithms
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ML
2000
ACM
144views Machine Learning» more  ML 2000»
13 years 6 months ago
MultiBoosting: A Technique for Combining Boosting and Wagging
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is abl...
Geoffrey I. Webb
ETS
2009
IEEE
98views Hardware» more  ETS 2009»
13 years 4 months ago
Increasing Robustness of SAT-based Delay Test Generation Using Efficient Dynamic Learning Techniques
Due to the increased speed in modern designs, testing for delay faults has become an important issue in the postproduction test of manufactured chips. A high fault coverage is nee...
Stephan Eggersglüß, Rolf Drechsler
ECCV
2006
Springer
14 years 8 months ago
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction
In recent years, nonlinear dimensionality reduction (NLDR) techniques have attracted much attention in visual perception and many other areas of science. We propose an efficient al...
Tony Lin, Hongbin Zha, Sang Uk Lee
ICML
2003
IEEE
14 years 7 months ago
Text Bundling: Statistics Based Data-Reduction
As text corpora become larger, tradeoffs between speed and accuracy become critical: slow but accurate methods may not complete in a practical amount of time. In order to make the...
Lawrence Shih, Jason D. Rennie, Yu-Han Chang, Davi...
SAC
2006
ACM
14 years 5 days ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal