The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
An approximate approach for training polynomial kernel SVMs in linear time
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Hi-index | 12.05 |
In this paper, a shell fitting space (SFS) is presented to map non-linearly separable data to linearly separable ones. A linear or quadratic transformation maps data into a new space for better classification, if the transformation method is properly guessed. This new SFS space can be of high or low dimensionality, and the number of dimensions is generally low and it is equal to the number of classes. The SFS method is based on fitting a hyper-plane or shell to the learning data or enclosing them into a hyper-surface. In the proposed method, the hyper-planes, curves, or cortex become the axis of the new space. In the new space a linear support vector machine (SVM) multi-class classifier is applied to classify the learn data.