Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
