k-nn

kd-trees
jd-trees: work on dimension of greatest variance
varaince pruning

experiment101

nb (symbolic data)

nb (numeric)
	gaussian
	unsupervised discretization
		rbst
		equal width
		equal frequency
	supervised discretization
		fayyad irrani

rule learning
	1R
	prism

feature selection
	infogain
	nomograms
	b^/(b+r)

instance selctions
	variance pruning
	pivot selection (fayola)

anomaly detection

which2

pareto optimization

active learning: find delta region between classes

bagging