Improving multiclass pattern recognition by the combination of two strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence2006Vol. 28(6), pp. 1001–1006
Citations Over TimeTop 10% of 2006 papers
Abstract
We present a new method of multiclass classification based on the combination of one-vs-all method and a modification of one-vs-one method. This combination of one-vs-all and one-vs-one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.
Related Papers
- → Classification of Damaged Road Types Using Multiclass Support Vector Machine (SVM)(2021)13 cited
- → Classification using support vector machines with graded resolution(2005)31 cited
- → Smoothing Support Vector Machines for e-Insensitive Regressi(2006)2 cited
- On Multiclass Support Vector Machines: One-Against-Half Approach(2010)
- → Shape Classification Using Multiple Classifiers with Different Feature Sets(2011)