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Analytical Observation for classification of Multilayer Neuron Models using different datasets
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Analytical Observation for classification of Multilayer Neuron Models using different datasets
Pankaj Kumar Kandpal1 , Ashish Mehta2
1 Department of Computer Science / Kumaun University, Nainital,Uttrakhand, India.
2 Department of Computer Science / Kumaun University, Nainital,Uttrakhand, India .

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 9-15, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.915

Online published on May 31, 2018

Copyright © Pankaj Kumar Kandpal, Ashish Mehta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: Pankaj Kumar Kandpal, Ashish Mehta, “Analytical Observation for classification of Multilayer Neuron Models using different datasets”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.9-15, 2018.

MLA Style Citation: Pankaj Kumar Kandpal, Ashish Mehta "Analytical Observation for classification of Multilayer Neuron Models using different datasets." International Journal of Computer Sciences and Engineering 6.5 (2018): 9-15.

APA Style Citation: Pankaj Kumar Kandpal, Ashish Mehta, (2018). Analytical Observation for classification of Multilayer Neuron Models using different datasets. International Journal of Computer Sciences and Engineering, 6(5), 9-15.
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Abstract :
In this paper, Multilayer Neuron model is used for classification of nonlinear problems. This conventional neuron model, is been taken for the analysis of while using different data sets. It is found, the Multilayer Neuron model showing its varying efficiency according to pattern of dataset. For analysis of model, various parameters of Artificial Neural Network like numbers of hidden neuron, number of attributes, learning rate, correlation coefficient, numbers of iteration, time elapse in training, mean square error etc. are being taken. After the analytical observation considering above various mentioned parameters, it is observed that there is no thump rule on behalf we can say that Multilayer Neuron Model follow the particular rule. The learning of model depends on the pattern of the dataset and the quality of data.
Key-Words / Index Term :
Multilayer Neuron, Classification, , analysis, Class
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