I stumbled upon this paper: https://www.researchgate.net/profile/Yavuz_Sari/publication/257406249_A_neural-network_committee_machine_approach_to_the_inverse_kinematics_problem_solution_of_robotic_manipulators/links/571cdbcb08ae6eb94d0e4b90/A-neural-network-committee-machine-approach-to-the-inverse-kinematics-problem-solution-of-robotic-manipulators.pdf?origin=publication_detail 6
The problem I have is with understanding how committee machines work. The only relevant info I find in this article is this:
“The   neural   networks   are   executed   simulta-
neously  for  the  given  input  data  and  their  outputs  are
evaluated  and  combined  to  produce  the  final  committee
output to obtain better generalization and performance. The
output combination module was often performed based on
simple functions on the outputs of individual members in
the  committee  machine,  such  as  majority  voting  for  clas-
sification  and  simple/weighted  averaging  for  regression,
without involving the input vectors of attributes”
So what does that mean, exactly? I have 10 different neural networks, compute their result, average them, and use that average in error estimation and then do backprop? Or do I simply train 10 different neural networks, then run my test sample through all of them and average the result?