Classification using Neural Network & Support Vector Machine for Sonar dataset
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2013 by IJCTT Journal|
|Volume-4 Issue-2 |
|Year of Publication : 2013|
|Authors :Ravi K Jade, L.K. Verma, Kesari Verma.|
Ravi K Jade, L.K. Verma, Kesari Verma."Classification using Neural Network & Support Vector Machine for Sonar dataset "International Journal of Computer Trends and Technology (IJCTT),V4(2):116-119 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Classification of the physical properties of sonar targets is one of the difficult tasks. The increased use of sonar motivated the researcher to produce cost effective and automated process for classification. Neural Network and Online Multiple Kernel Learning (OMKL), that aims to learn a kernel based prediction function from a pool of predefined kernels in an online learning fashion. OMKL is generally more challenging than typical online learning because both the kernel classifiers and their linear combination weights must be learned simultaneously . We experimented the sonar dataset for deterministic and stochastic using online multiple kernel learning. The online Support vector machine and Neural Network techniques are applied to classify sonar data.
 http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+M ines+vs.+Rocks)
 Rong Jin, Steven C.H. Hoi, Tianbao Yang. Online Multiple Kernel Learning: Algorithms andMistake Bounds. The 21st International Conference on Algorithmic Learning Theory. Canberra, Australia, 6~8 Oct, 2010. pp. 390-404.
 R. Paul Gorman, T. J. Sejnowski. Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets. Neural Networks. Vol. 1, pp. 75-89, 1988.
 Louis Nicolas Atllah. Learning from Sonar Data for the Classification of Underwater Seabeds. Ph.D. Thesis.
 Mehmet Gonen, Ethem Alpaydin. Multiple Kernel Learning Algorithms. Journal of Machine Learning Research 12 (2011) 2211- 2268
Keywords—Signal classification, support vector machine classifier, Sonar data classification.