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Abstract:
In this paper, we question the necessity of levels of
expert-guided abstraction in learning hard, statistically neutral
classification tasks. We focus on two tasks, date calculation and
parity-12, that are claimed to require intermediate levels of
abstraction that must be defined by a human expert. We challenge
this claim by demonstrating empirically that a single hidden-layer
BP-SOM
network can learn both tasks without guidance. Moreover, we
analyze the network's solution for the parity-12 task and show that
its solution makes use of an elegant intermediary checksum
computation.
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