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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.
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We then propose an efficient method for pruning large part of input combinations.
Fortunately, the Viterbi algorithm [ 25] provides an efficient method for pruning the number of probability calculations needed to find the most likely path through the model.
To avoid this, we propose two methods of pruning.
We note that both methods of pruning lead to very similar outcomes.
Both methods of pruning use different rationale and have different drawbacks.
Methods based on within-node deviance are hired for pruning.
Division in spring is the recommended form of propagation for the species, and regular deadheading is the recommended method of pruning.
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