By Topic

Learning class regions by the union of ellipsoids

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kositsky, M. ; Dept. of Appl. Math. & Comput. Sci., Weizmann Inst. of Sci., Rehovot, Israel ; Ullman, S.

In many classification schemes objects are represented as points in multi-dimensional feature spaces. The classification scheme then attempts to discriminate between regions in the space occupied by objects of different classes. The performance of the classification method often depends on the shape of the class regions, e.g., whether or not they are linearly separable. In many practical cases, class regions have the structure of smooth low-dimensional manifolds. We develop a novel classification scheme that covers each class region by a set of ellipsoids that are oriented along the local orientation of the manifold. The scheme learns the class regions from sequential presentation of samples, and the ellipsoids are created and modified incrementally during the learning. In high dimensional feature spaces the ellipsoids cover can become significantly more efficient than alternative classification schemes

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996