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» Semi-Supervised Dimensionality Reduction
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NLDB
2005
Springer
15 years 9 months ago
On Some Optimization Heuristics for Lesk-Like WSD Algorithms
For most English words, dictionaries give various senses: e.g., “bank” can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a gi...
Alexander F. Gelbukh, Grigori Sidorov, Sang-Yong H...
ISDA
2010
IEEE
15 years 2 months ago
Feature selection is the ReliefF for multiple instance learning
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature sel...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
ICML
2004
IEEE
16 years 4 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
MCS
2007
Springer
15 years 10 months ago
Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification
In this paper we present a fusion technique for Support Vector Machine (SVM) scores, obtained after a dimension reduction with Bilateralprojection-based Two-Dimensional Principal C...
Ángel Serrano, Isaac Martín de Diego...
PR
2008
129views more  PR 2008»
15 years 4 months ago
A comparison of generalized linear discriminant analysis algorithms
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
Cheong Hee Park, Haesun Park