SmartSifter: Online Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms

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Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.

Author
Lizhen Nie <nie_lizhen@yahoo.com>
Date of publication
2016-09-14 18:50:50
Maintainer
Lizhen Nie <nie_lizhen@yahoo.com>
License
GPL (>= 2)
Version
0.1.0

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Man pages

delta
delta
HellingerScore
HellingerScore
HellingerScoreOne
HellingerScoreOne
InitializeCell
InitializeCell
InputOneSample
InputOneSample
InputSample
InputSample
LogLoss
LogLoss
LogLossOne
LogLossOne
Test
Test
Train
Train
UpdateConst
UpdateConst
WhichCell
WhichCell

Files in this package

SmartSifter
SmartSifter/inst
SmartSifter/inst/CITATION
SmartSifter/tests
SmartSifter/tests/testthat.R
SmartSifter/tests/testthat
SmartSifter/tests/testthat/test-Train.R
SmartSifter/tests/testthat/test-Test.R
SmartSifter/NAMESPACE
SmartSifter/R
SmartSifter/R/LogLoss.R
SmartSifter/R/delta.R
SmartSifter/R/Train.R
SmartSifter/R/Test.R
SmartSifter/R/InitializeCell.R
SmartSifter/R/InputSample.R
SmartSifter/R/LogLossOne.R
SmartSifter/R/UpdateConst.R
SmartSifter/R/HellingerScore.R
SmartSifter/R/HellingerScoreOne.R
SmartSifter/R/InputOneSample.R
SmartSifter/R/WhichCell.R
SmartSifter/MD5
SmartSifter/DESCRIPTION
SmartSifter/man
SmartSifter/man/InputSample.Rd
SmartSifter/man/InputOneSample.Rd
SmartSifter/man/HellingerScoreOne.Rd
SmartSifter/man/UpdateConst.Rd
SmartSifter/man/LogLossOne.Rd
SmartSifter/man/Test.Rd
SmartSifter/man/LogLoss.Rd
SmartSifter/man/InitializeCell.Rd
SmartSifter/man/WhichCell.Rd
SmartSifter/man/Train.Rd
SmartSifter/man/HellingerScore.Rd
SmartSifter/man/delta.Rd