Title :
On the infeasibility of training neural networks with small mean-squared error
Author_Institution :
Dept. of Math., Yale Univ., New Haven, CT, USA
fDate :
11/1/1998 12:00:00 AM
Abstract :
We demonstrate that the problem of training neural networks with small mean-squared error is computationally intractable. This answers a question posed by Jones (1997)
Keywords :
learning (artificial intelligence); mean square error methods; neural nets; neural networks training; small mean-squared error neural networks; Computer errors; Computer networks; Computer science; Interpolation; Mathematics; NP-hard problem; Neural networks; Polynomials; Seminars; Springs;
Journal_Title :
Information Theory, IEEE Transactions on
