A New Multi-Class SVM Based on a Uniform Convergence Result

Authors:
André Elisseeff;Hélène Paugam-Moisy
Affiliations:
-;-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Year:
2000

Citing 0
Cited 8

Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization

PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Kernel Based Image Classification

ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
On the algorithmic implementation of multiclass kernel-based vector machines

The Journal of Machine Learning Research
A trainable feature extractor for handwritten digit recognition

Pattern Recognition
Incorporating prior knowledge in support vector machines for classification: A review

Neurocomputing
A fuzzy integral method of applying support vector machine for multi-class problem

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Application of global optimization methods to model and feature selection

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We introduce a new support vector machine devoted to the approximation of multi-class discriminant functions. Its training procedure consists in minimizing a new expression of the guaranteed risk. This bound is significantly tighter than the former ones, which should make the implementation of the structural risk minimization inductive principle in the context of multi-class discrimination better grounded.