Abstract
In a standard classification framework, a discriminating rule is usually built from a trustworthy set of labeled units. In this context, test observations will be automatically classified as to have arisen from one of the known groups encountered in the training set, without the possibility of detecting previously unseen classes. To overcome this limitation, an adaptive semi-parametric Bayesian classifier is intro- duced for modeling the test units, where robust knowledge is extracted from the training set and incorporated within the priors’ model specification. A successful application of the proposed approach in a real-world problem is addressed.
Original language | English |
---|---|
Title of host publication | Book of Short Papers SIS 2020 |
Pages | 655-660 |
Number of pages | 6 |
Publication status | Published - 2020 |
Event | SIS 2020 - Pisa Duration: 23 Jun 2020 → 26 Jun 2020 |
Conference
Conference | SIS 2020 |
---|---|
City | Pisa |
Period | 23/6/20 → 26/6/20 |
Keywords
- Bayesian mixture model
- Bayesian sdaptive learning
- Stick-breaking prior
- Supervised classification
- Unobserved classes