Titlebook: Computational Learning Theory; 4th European Confere Paul Fischer,Hans Ulrich Simon Conference proceedings 1999 Springer-Verlag Berlin Heide

chastise

辮子帶來幫助

finite

Commonwealth

Second-Order-Faktorenanalyse (SFA)nits, it is NP-hard to find such a network that makes mistakes on a proportion smaller than .. of the examples, for some constant .. We prove a similar result for the problem of approximately minimizing the quadratic loss of a two-layer network with a sigmoid output unit.

極微小

包庇

同步左右

軍火

Anticoagulants

創(chuàng)作

Regularized Principal Manifoldsapproach. 2) We derive uniform convergence bounds and hence bounds on the learning rates of the algorithm. In particular, we give good bounds on the covering numbers which allows us to obtain a nearly optimal learning rate of order . for certain types of regularization operators, where . is the sample size and α an arbitrary positive constant.