Dynamic node architecture learning: An information theoretic approach

✍ Scribed by Eric B. Bartlett

• Book ID: 103926561
• Publisher: Elsevier Science
• Year: 1994
• Tongue: English
• Weight: 981 KB
• Volume: 7
• Category: Article
• ISSN: 0893-6080
• DOI: 10.1016/0893-6080(94)90061-2

✦ Synopsis

Typwally, artifictal neural network (ANN) trammg schemes requtre network stze to be set before learnmg ts tnmated The learning speed and generahzation charactenstws of ANNs are, however, dependent on thts pretraming selection of the network archttecture The trammg and generalizatton vtabthty of a spectfic network can, therefore, only be evaluated posttrammg Thts work presents an mformatton theoretw method that allevmtes this predwament by buddmg the approprtate network archttecture dynamwally durmg the trammg process. The method, called dynamtc node archttecture learnmg ( DNAL ), ehmmates the need to select network size before trammg Examples illustrate the use and advantages of the mformatlon theorettc DNAL approach over stattc architecture learning ( SAL ).