Optimizing Feedforward Neural Network of Classification Problems
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چکیده
Many researchers have proposed pruning algorithms in numerous ways to optimize the network architecture (Castellano et al., 1997; Ahmmed et al., 2007; Henrique et al., 2000; Ponnapalli et al., 1999). Reed (1993) and Engelbrecht (2001) have given detailed surveys of pruning algorithms. Each algorithm has its own advantages and limitations. Some algorithms (Engelbrecht, 2001; Xing & Hu, 2009) prune both irrelevant input neurons and hidden neurons of the network and some algorithms (Zeng & Yeung, 2006) prune irrelevant hidden neurons only. Real-world applications prefer simpler and more efficient methods. But a significant drawback of most standard methods consist in their low efficiency. For example the main
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