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CGF
2011
12 years 10 months ago
Intrinsic Shape Matching by Planned Landmark Sampling
Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have bee...
Art Tevs, Alexander Berner, Michael Wand, Ivo Ihrk...
CIKM
2010
Springer
13 years 4 months ago
Online stratified sampling: evaluating classifiers at web-scale
Deploying a classifier to large-scale systems such as the web requires careful feature design and performance evaluation. Evaluation is particularly challenging because these larg...
Paul N. Bennett, Vitor R. Carvalho
JMLR
2006
99views more  JMLR 2006»
13 years 6 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
SAC
2006
ACM
14 years 20 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
CORR
2011
Springer
155views Education» more  CORR 2011»
13 years 1 months ago
Reconciling Compressive Sampling Systems for Spectrally-sparse Continuous-time Signals
The Random Demodulator (RD) and the Modulated Wideband Converter (MWC) are two recently proposed compressed sensing (CS) techniques for the acquisition of continuous-time spectral...
Michael A. Lexa, Mike E. Davies, John S. Thompson