Mutual information based distance measures for classification and content recognition with applications to genetics

Possibilities of using mutual information for classification and content recognition are exploited. Two different mutual information based distance measures are proposed, one for classification and one for content recognition. The measure proposed for classification is shown to be a metric. The influence of compression based estimation methods on the proposed measures is investigated. Several examples of successful applications in the field of genetics are presented.