Courtney Keeler, Alexa Colgrove Curtis
Case-Control Studies
The American journal of nursing
work driven practical question corrections artificial intelligence ai errors corrections quick noniterative solve problem without modification legacy ai system propose special external devices correctors elementary correctors consist two parts classifier separates situations high risk error situations legacy ai system works well new decision recommended situations potential errors input signals correctors inputs legacy ai system internal signals outputs intrinsic dimensionality data high enough classifiers correction small number errors simple according blessing dimensionality effects even simple robust fishers discriminants used oneshot learning ai correctors stochastic separation theorems provide mathematical basis oneshort learning however number correctors needed grows cluster structure data becomes important new family stochastic separation theorems required refuse classical hypothesis regularity data distribution assume data rich finegrained structure many clusters corresponding peaks probability density new stochastic separation theorems data finegrained structure formulated proved basis theorems multicorrectors granular data proposed advantages multicorrector technology demonstrated examples correcting errors learning new classes objects deep convolutional neural network cifar10 dataset key problems nonclassical highdimensional data analysis reviewed together basic preprocessing steps including correlation transformation supervised principal component analysis pca semisupervised pca transfer component analysis new domain adaptation pca