title: Multi-output vs. multiple single-output neural networks for classification
body: <p>Is there a theoretical argument in favor of or against using a single multi-output NN to do multi-class classification vs. using several one-vs-all NNs ?</p>
  
  <p>In both cases once all output values are obtained, the same decision rule is used: the output with highest activation "wins" and decides which class the prediction returns.</p>
  
  <p>But I wonder whether -and why- it's better or worse to have all outputs calculated on the same NN rather than separately.</p>