Development of a novel stopping technique for optimization

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Title: Development of a novel stopping technique for optimization
Author: Iyer, Mahesh Subramaniam
Abstract: Neural networks are being used widely in areas of process control , pattern recognition , etc . The possibility of improving the efficiency of data utilization in neural network training and automating the decision to stop training , using a novel Steady -state Identifier (SSID ) algorithm , have been investigated . One conclusion is that complete automation of the decision criterion to stop training is probably beyond the realm of possibility and human judgment seems unavoidable . However , as a beneficial outcome of this study , a technique has been developed to determine the number of neural network training repetitions to guarantee the convergence of the training algorithm within a certain vicinity ofthe global optimum of the objective function , with a desired level of confidence . The concept used is the weakest -link -in -the chain analysis . As another outcome , a novel approach of stopping neural network training has been developed . In this technique , a random fraction ofthe training set data is sampled at each epoch . The error on the random fraction is tested for its attainment of steady -state or otherwise using either a novel Steady -State Identifier or equivalently by visual observation by a human operator . Training is stopped when the error on the random fraction attains Steady -State . This technique , in general , is more cost effective than cross -validation . The overall developments are perfectly general and can also be applied to optimization problems other than neural network training .
URI: http : / /hdl .handle .net /2346 /12535
Date: 1997-12


Iyer, Mahesh Subramaniam Development of a novel stopping technique for optimization. Doctoral dissertation, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /12535 .

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