In addition, Sreenivas et al., in [17], Roche, in [18], and Wang et al., in [19], developed the methods for pruning the input-output at the same time.
A variety of competing methods for pruning the DFTs in arbitrary (non-sparse) computational scenarios have been addressed in the literature (see [1, 3, 13 19, 23, 24]).
Existing methods for pruning uninteresting patterns have only begun to automate the knowledge acquisition step (which is required for subjective measures of interestingness), hence leaving a serious bottleneck.
Hack- stadt and Hess [ 18] investigated three filtering methods for pruning genes before statistical tests: MAS detection call, variance, and average signal.
Division in spring is the recommended form of propagation for the species, and regular deadheading is the recommended method of pruning.
Methods based on within-node deviance are hired for pruning.
This paper proposes an algorithm for pruning ELM networks by using regularized regression methods, thus obtaining a suitable number of the hidden nodes in the network architecture.
To avoid this, we propose two methods of pruning.
We note that both methods of pruning lead to very similar outcomes.
