I want to know more--efficient multi-class incremental learning using Gaussian processes

Authors:
A. Lütz;E. Rodner;J. Denzler
Affiliations:
Chair for Computer Vision, Friedrich Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Friedrich Schiller University of Jena, Jena, Germany;Chair for Computer Vision, Friedrich Schiller University of Jena, Jena, Germany
Venue:
Pattern Recognition and Image Analysis
Year:
2013

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Abstract

One of the main assumptions in machine learning is that sufficient training data is available in advance and batch learning can be applied. However, because of the dynamics in a lot of applications, this assumption will break down in almost all cases over time. Therefore, classifiers have to be able to adapt themselves when new training data from existing or new classes becomes available, training data is changed or should be even removed. In this paper, we present a method allowing for efficient incremental learning of a Gaussian process classifier. Experimental results show the benefits in terms of needed computation times compared to building the classifier from the scratch. In addition we highlight the general benefits of incremental learning.