  Ensemble models are widely used to solve complex tasks by their decomposition
into multiple simpler tasks, each one solved locally by a single member of the
ensemble. Decoding of error-correction codes is a hard problem due to the curse
of dimensionality, leading one to consider ensembles-of-decoders as a possible
solution. Nonetheless, one must take complexity into account, especially in
decoding. We suggest a low-complexity scheme where a single member participates
in the decoding of each word. First, the distribution of feasible words is
partitioned into non-overlapping regions. Thereafter, specialized experts are
formed by independently training each member on a single region. A classical
hard-decision decoder (HDD) is employed to map every word to a single expert in
an injective manner. FER gains of up to 0.4dB at the waterfall region, and of
1.25dB at the error floor region are achieved for two BCH(63,36) and (63,45)
codes with cycle-reduced parity-check matrices, compared to the previous best
result of the paper "Active Deep Decoding of Linear Codes".
