Predicting Good Probabilities With Supervised Learning
Set m^k;l = (wk;i 1m^k;i 1 +wi;lm^i;l)=wk;l Replace m^k;i 1 and m^i;l with m^k;l 4 Output the stepwise const. function: m^(f) = m^i;j, for fi < f fj where m is an isotonic (monotonically increasing) func-tion. Then, given a train set (fi;yi), the Isotonic Regres-sion problem is finding the isotonic function m^ such that
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