Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
712946 | IFAC-PapersOnLine | 2015 | 6 Pages |
Abstract
The accuracy of classification and regression tasks based on data driven models, such as Neural Networks or Support Vector Machines, relies to a good extent on selecting proper data for designing these models that covers the whole input ranges in which they will be employed. The convex hull algorithm is applied as a method for data selection; however the use of conventional implementations of this method in high dimensions, due to its high complexity, is not feasible. In this paper, we propose a randomized approximation convex hull algorithm which can be used for high dimensions in an acceptable execution time.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics