Name
Abstract | ||
|---|---|---|
We propose a probabilistic enhancement of standard {em kernel Support Vector Machines} for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available on each datum. In the present paper, we specifically consider Gaussian distributions to model uncertainty. Thereby, our data consist of pairs $(x_i,Sigma_i)$, $iin{1,ldots,N}$, along with an indicator $y_iin{-1,1}$ to declare membership in one of two categories for each pair. These pairs may be viewed to represent the mean and covariance, respectively, of random vectors $xi_i$ taking values in a suitable linear space (typically ${mathbb R}^n$). Thus, our setting may also be viewed as a modification of Support Vector Machines to classify distributions, albeit, at present, only Gaussian ones. We outline the formalism that allows computing suitable classifiers via a natural modification of the standard ``kernel trick.u0027u0027 The main contribution of this work is to point out a suitable kernel function for applying Support Vector techniques to the setting of uncertain data for which a detailed uncertainty description is also available (herein, ``Gaussian pointsu0027u0027). |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Learning | Journal |
Volume | Citations | PageRank |
abs/1904.06762 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongxin Chen | 1 | 99 | 31.89 |
| Tryphon T. Georgiou | 2 | 211 | 36.71 |
| Allen Tannenbaum | 3 | 3629 | 409.15 |