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Multi-class Classification with Dependent Gaussian Processes

Andriluka, Mykhaylo and Weizsäcker, Lorenz and Hofmann, Thomas (2007):
Multi-class Classification with Dependent Gaussian Processes.
In: 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007), [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2007
Creators: Andriluka, Mykhaylo and Weizsäcker, Lorenz and Hofmann, Thomas
Title: Multi-class Classification with Dependent Gaussian Processes
Language: German
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.

Title of Book: 12th International Conference on Applied Stochastic Models and Data Analysis (ASMDA 2007)
Divisions: 20 Department of Computer Science
Date Deposited: 31 Dec 2016 10:04
Identification Number: asmda2007andriluka
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