We study the geometry of datasets, using an extension of the Fisher linear discriminant to the case of singular covariance, and a new regularization procedure. A dataset is called linearly separable if its different clusters can be reliably separated by a linear hyperplane. We propose a measure of l
โฆ LIBER โฆ
On linear separability of data sets in feature space
โ Scribed by Degang Chen; Qiang He; Xizhao Wang
- Book ID
- 113815290
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 250 KB
- Volume
- 70
- Category
- Article
- ISSN
- 0925-2312
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## Abstract In this paper the concepts of strictly convex and uniformly convex normed linear spaces are extended to metric linear spaces. A relationship between strict convexity and uniform convexity is established. Some existence and uniqueness theorems on best approximation in metric linear space