@inproceedings{Luetz11:EIL,
    type = {inproceedings},
    key = {Luetz11:EIL},
    title = {Efficient Multi-Class Incremental Learning Using Gaussian Processes},
    author = {Alexander Lütz and Erik Rodner and Joachim Denzler},
    booktitle = {Open German-Russian Workshop on Pattern Recognition and Image Understanding (OGRW)},
    year = {2011},
    pages = {182-185},
    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 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.},
    groups = {gaussianprocesses,lifelonglearning,incrementallearning},
    keywords = {OCC_IL_TL},
    website = {http://www.inf-cv.uni-jena.de/incremental_learning.html},
}