Abstract
Swap-1, a state-of-the-art system for learning decision rules from data, is presented. The method embodied in Swap-1 generates reduced-complexity solutions by inducing compact solutions in larger dimensions where many rules might be needed to make accurate predictions. For many applications, such systems can automatically construct relatively compact rule sets with highly predictive performance. >
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