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DECISION TREES DO NOT GENERALIZE TO NEW VARIATIONS
2010
Computational intelligence
The family of decision tree learning algorithms is among the most widespread and studied. Motivated by the desire to develop learning algorithms that can generalize when learning highly varying functions such as those presumably needed to achieve artificial intelligence, we study some theoretical limitations of decision trees. We demonstrate formally that they can be seriously hurt by the curse of dimensionality in a sense that is a bit different from other nonparametric statistical methods,
doi:10.1111/j.1467-8640.2010.00366.x
fatcat:tqyvj6kr2bhm7ggra6hg7e3om4