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Updated: Jan 23, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
Published on: June 1, 2015
Learning the Unlearnable
Dan Nabutovsky1, Eytan Domany2
1Department of Electronics, Weizmann Institute of Science, Rehovot 76100, Israel.
Abstract:
We present a local perceptron-learning rule that either converges to a solution, or establishes linear nonseparability. We prove that when no solution exists, the algorithm detects this in a finite time (number of learning steps). This time is polynomial in typical cases and exponential in the worst case, when the set of patterns is nonstrictly linearly separable. The algorithm is local and has no arbitrary parameters.
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