A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Low-Interference Output Partitioning for Neural Network Training
2013
Journal of Clean Energy Technologies
This paper presents a new output partitioning approach with the advantages of constructive learning and output parallelism. Classification error is used to guide the partitioning process so that several smaller sub-dimensional data sets are divided from the original data set. When training each sub-dimensional data set in parallel, the smaller constructively trained sub-network uses the whole input vector and produces a portion of the final output vector where each class is represented by one
doi:10.7763/jocet.2013.v1.75
fatcat:oshjfwruwfeqpovtp2fkrvy4q4