The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Multiple order gradient feature for macro-invertebrate identification using support vector machines
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Classification and retrieval on macroinvertebrate image databases
Computers in Biology and Medicine
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Support vector machines are a relatively new classification method which has nowadays established a firm foothold in the area of machine learning. It has been applied to numerous targets of applications. Automated taxa identification of benthic macroinvertebrates has got generally very little attention and especially using a support vector machine in it. In this paper we investigate how the changing of a kernel function in an SVM classifier effects classification results. A novel question is how the changing of a kernel function effects the number of ties in a majority voting method when we are dealing with a multi-class case. We repeated the classification tests with two different feature sets. Using SVM, we present accurate classification results proposing that SVM suits well to the automated taxa identification of benthic macroinvertebrates. We also present that the selection of a kernel has a great effect on the number of ties.