Neural networks and statistical techniques: A review of applications |
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| Authors: | Mukta Paliwal Usha A. Kumar |
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| Affiliation: | 1. Department of Mathematics, University of Oslo, Niels Henrik Abels hus Moltke Moes vei 35, Oslo 0851, Norway;2. Statistical Analysis, Machine Learning and Image Analysis, Norwegian Computing Center, Gaustadalleen 23a, Oslo 0373, Norway;3. Group Risk Modelling, DNB ASA, Dronning Eufemias gate 30, Oslo 0191, Norway;1. Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom;2. Bristol Enterprise Research and Innovation Centre (BERIC), University of the West of England, Bristol, United Kingdom;3. Department of International Strategy & Business, The University of Northampton, United Kingdom;4. Bristol Enterprise Research and Innovation Centre (BERIC), University of the West of England, Bristol, United Kingdom;5. School of Built Environment and Engineering, Leeds Becket University, Leeds, United Kingdom;6. Bristol Enterprise Research and Innovation Centre (BERIC), University of the West of England, Bristol, United Kingdom |
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| Abstract: | Neural networks are being used in areas of prediction and classification, the areas where statistical methods have traditionally been used. Both the traditional statistical methods and neural networks are looked upon as competing model-building techniques in literature. This paper carries out a comprehensive review of articles that involve a comparative study of feed forward neural networks and statistical techniques used for prediction and classification problems in various areas of applications. Tabular presentations highlighting the important features of these articles are also provided. This study aims to give useful insight into the capabilities of neural networks and statistical methods used in different kinds of applications. |
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