Andriluka, Mykhaylo ; Weizsäcker, Lorenz ; Hofmann, Thomas (2007)
Multi-class Classification with Dependent Gaussian Processes.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present a novel multi-output Gaussian process model for multi-class classification. We build on the formulation of Gaussian processes via convolution of white Gaussian noise processes with a parameterized kernel and present a new class of multi-output covariance functions. The latter allow for greater flexibility in modelling relationships between outputs while being parsimonious with regard to the number of model parameters. We apply the model to multi-class Gaussian process classification using a sparse approximation based on the informative vector framework and investigate, both analytically as well as empirically, a scenario where our multi-class classifier performs better than combining independently trained binary classifiers.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Andriluka, Mykhaylo ; Weizsäcker, Lorenz ; Hofmann, Thomas |
| Art des Eintrags: | Bibliographie |
| Titel: | Multi-class Classification with Dependent Gaussian Processes |
| Sprache: | Deutsch |
| Publikationsjahr: | 2007 |
| Buchtitel: | 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007) |
| Kurzbeschreibung (Abstract): | We present a novel multi-output Gaussian process model for multi-class classification. We build on the formulation of Gaussian processes via convolution of white Gaussian noise processes with a parameterized kernel and present a new class of multi-output covariance functions. The latter allow for greater flexibility in modelling relationships between outputs while being parsimonious with regard to the number of model parameters. We apply the model to multi-class Gaussian process classification using a sparse approximation based on the informative vector framework and investigate, both analytically as well as empirically, a scenario where our multi-class classifier performs better than combining independently trained binary classifiers. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 31 Dez 2016 10:04 |
| Letzte Änderung: | 16 Mai 2018 12:47 |
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