Machine learning using support vector machines
Marimuthu Swami Palaniswami, Alistair Shilton, Daniel Ralph, Brendan Owen · Minerva Access (University of Melbourne) · 2000
Machine learning invokes the imagination of many scientific minds due to its potential to solve complex and difficult real world problems. This paper gives methods of constructing machine learning tools using Support Vector Machines (SVMs). We first give a simple example to illustrate the basic concept and then demonstrate further with a practical problem. The practical problem is concerned with electronic monitoring of fishways for automatic counting of different fish species for the purpose of environmental management in Australian rivers. The results illustrate the power of the SVM approaches on the sample problem and their computational attractiveness for practical implementations.