The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
A Comparative Study of Kernels for the Multi-class Support Vector Machine
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 02
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
SVM-Based Automatic Classification of Musical Instruments
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 03
Classification and retrieval on macroinvertebrate image databases
Computers in Biology and Medicine
Comparison of multiclass SVM decomposition schemes for visual object recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Instructional Video Content Analysis Using Audio Information
IEEE Transactions on Audio, Speech, and Language Processing
DAGSVM vs. DAGKNN: an experimental case study with benthic macroinvertebrate dataset
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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This paper investigates automated benthic macroinvertebrate identification and classification with multi-class support vector machines. Moreover, we examine, how the feature selection effects results, when one-vs-one and one-vsall methods are used. Lastly, we explore what happens for the number of tie situations with different kernel function selections. Our wide experimental tests with three feature sets and seven kernel functions indicated that one-vs-one method suits best for the automated benthic macroinvertebrate identification. In addition, we obtained clear differences to the number of tie situations with different kernel funtions. Furthermore, the feature selection had a clear influence on the results.