PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Exploiting Cluster-Structure to Predict the Labeling of a Graph
Mark Herbster
In: ALT 2008, 13-16 October, Budapest, Hungary.


The nearest neighbor and the perceptron algorithms are in- tuitively motivated by the aims to exploit the “cluster” and “linear sep- aration” 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 la- beling 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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5146
Deposited By:Mark Herbster
Deposited On:24 March 2009