The nearest neighbor and the perceptron algorithms are intuitively motivated by the aims to exploit the "cluster" and "linear separation" structure of the data to be classified, respectively. We develop a new online perceptron-like algorithm, Pounce, to exploit both types of structure. We refine the usual margin-based analysis of a perceptron-like algorithm to now additionally reflect the cluster-structure of the input space. We apply our methods to study the problem of predicting the labeling of a graph. We find that when both the quantity and extent of the clusters are small we may improve arbitrarily over a purely margin-based analysis. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Herbster, M. (2008). Exploiting cluster-structure to predict the labeling of a graph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5254 LNAI, pp. 54–69). https://doi.org/10.1007/978-3-540-87987-9_9
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