We obtained an analytical expression for the computational complexity of many layered committee machines with a finite number of hidden layers (L < 8) using the generalization complexity measure introduced by Franco et al (2006) IEEE Trans. Neural Netw. 17 578. Although our result is valid in the large-size limit and for an overlap synaptic matrix that is ultrametric, it provides a useful tool for inferring the appropriate architecture a network must have to reproduce an arbitrary realizable Boolean function.
|Number of pages||1|
|Journal||Journal of Physics A: Mathematical and Theoretical|
|Publication status||Published - 5 Nov 2010|
Bibliographical note© 2010 IOP Publishing Ltd.
- computational complexity
- layered committee machines
- generalization complexity measure
- overlap synaptic matrix
- Boolean function
Neirotti, J. P., & Franco, L. A. (2010). Computational capabilities of multilayer committee machines. Journal of Physics A: Mathematical and Theoretical, 43(44), 445103. . https://doi.org/10.1088/1751-8113/43/44/445103