RELIABLE EVALUATION OF NEURAL NETWORKS

被引：4
|
作者：
BURTON, RM [1 ]
FARIS, WG [1 ]
机构：
[1] UNIV ARIZONA,TUCSON,AZ 85721
来源：
NEURAL NETWORKS | 1991年 / 4卷 / 03期
关键词：
FEEDFORWARD NETWORK; CONTINUOUS THRESHOLD; NETWORK EVALUATION; VALID GENERALIZATION; EMPIRICAL PROCESS; SAMPLE MEAN; LARGE DEVIATION; VAPNIK-CHERVONENKIS THEORY;
D O I：
10.1016/0893-6080(91)90078-J
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The setting is a neural network receiving random inputs. These may be thought of as providing data for evaluation trials. The inputs may be the same as the inputs used on the training trials that produced the network. The main result is an explicit bound on the probability that the average output in the evaluation trials differs from the expected long-range output.
引用
收藏
页码：411 / 415
页数：5
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