Complexity estimates based on integral transforms induced by computational units.
Integral transforms with kernels corresponding to computational units are exploited to derive estimates of network complexity. The estimates are obtained by combining tools from nonlinear approximation theory and functional analysis together with representations of functions in the form of infinite neural networks. The results are applied to perceptron networks.
Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodárenskou Věží 2, 18207 Prague, Czech Republic. vera@cs.cas.cz
Věra
V
Kůrková
D019295:Computational Biology / Q000379:methods*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics