Abstract
In many fields there are situations encountered, where a function has to be estimated to determine its output under new conditions. Some functions have one output corresponding to differing input patterns. Such types of functions are difficult to map using a function approximation technique such as that employed by the Multilayer Perceptron Network. Hence to reduce this functional mapping to Single Pattern-to-Single Pattern type of condition, and then effectively estimate the function, we employ classification techniques such as the Support Vector Machines. This paper describes in detail such a combined technique, which shows excellent results for practical applications.
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© 2004 Springer-Verlag Berlin Heidelberg
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Pakka, V.H. (2004). Approximation of Multi-pattern to Single-Pattern Functions by Combining FeedForward Neural Networks and Support Vector Machines. In: Manandhar, S., Austin, J., Desai, U., Oyanagi, Y., Talukder, A.K. (eds) Applied Computing. AACC 2004. Lecture Notes in Computer Science, vol 3285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30176-9_14
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DOI: https://doi.org/10.1007/978-3-540-30176-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23659-7
Online ISBN: 978-3-540-30176-9
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