Please use this identifier to cite or link to this item:
|Title:||A reduced multivariate polynomial based neural network model pattern classifier for freeway incident detection|
|Authors:||Srinivasan, D. |
|Citation:||Srinivasan, D., Sharma, V. (2007). A reduced multivariate polynomial based neural network model pattern classifier for freeway incident detection. IEEE International Conference on Neural Networks - Conference Proceedings : 2366-2372. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2007.4371328|
|Abstract:||This paper proposes a reduced multivariate polynomial based pattern classifier using a three-layer Neural Network with linear regularized least square algorithm as an adjunct operation, for freeway incident detection. Freeway incident detection can be seen as a two class pattern classification problem where the rate of convergence is a major concern besides accurate classification. The reduced multivariate polynomial based model is particularly suitable for simple classification problems with small number of features and with large number of patterns available. Freeway incident detection is one such class of problem. Smaller number of terms in the reduced model compared to original full multivariate polynomial model results in small network size and increased speed of convergence, thus making it useful for freeway incident detection. SVD based and gradient descent based least square estimators were used separately and encouraging results were obtained compared to other classification strategies used for freeway incident detection allowing for further work on the use of this model with improvement in the algorithm. ©2007 IEEE.|
|Source Title:||IEEE International Conference on Neural Networks - Conference Proceedings|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 26, 2019
WEB OF SCIENCETM
checked on Mar 18, 2019
checked on Dec 29, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.