Maximum margin equalizers trained with the Adatron algorithm
Signal Processing
A novel and quick SVM-based multi-class classifier
Pattern Recognition
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
The method for solving two types of errors in customer segmentation on unbalanced data
Proceedings of the 10th international conference on Electronic commerce
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A Parallel Implementation of Error Correction SVM with Applications to Face Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
Complex-valued support vector classifiers
Digital Signal Processing
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Face recognition based on gabor enhanced marginal fisher model and error correction SVM
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
An SVM classification algorithm with error correction ability applied to face recognition
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Information retrieval and classification with wavelets and support vector machines
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Face recognition based on gabor-enhanced manifold learning and SVM
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Hi-index | 35.68 |
Two enhancements are proposed to the application and theory of support vector machines. The first is a method of multicategory classification based on the binary classification version of the support vector machine (SVM). The method, which is called the M-ary SVM, represents each category in binary format, and to each bit of that representation is assigned a conventional SVM. This approach requires only [log2(K)] SVMs, where K is the number of classes. We give an example of classification on an octaphase-shift-keying (8-PSK) pattern space to illustrate the main concepts. The second enhancement is that of adding equality constraints to the conventional binary classification SVM. This allows pinning the classification boundary to points that are known a priori to lie on the boundary. Applications of this method often arise in problems having some type of symmetry, We present one such example where the M-ary SVM is used to classify symbols of a CDMA two-user, multiuser detection pattern space