Title :
Prior weights in adaptive pattern classification
Author_Institution :
Inst. of Math. & Inf., Vilnius, Lithuania
Abstract :
Nonrandom initial values of the weight vector can contain useful information. A weighted combination of the initial and final values of the weight vector can help to utilise this information. In adaptive training, at a start, we need to scale the initial weights and to stop training earlier before a minimum of the cost function is obtained. In order to weight, scale or to stop training optimally one needs to know the accuracy of determination of initial and final weights
Keywords :
feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; pattern classification; statistical analysis; adaptive pattern classification; adaptive training; nonrandom initial values; prior weights; weight vector; Cost function; Databases; Decision making; Hardware; Informatics; Information analysis; Mathematics; Pattern classification; Random variables; Training data;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906245