Efficient Adaptation of Structure Metrics in Prototype-Based Classification

Abstract

More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector quantization (RLVQ). The adjustment of metric parameters like relevance weights for basic structural elements constitutes a crucial issue therein, and first methods to automatically learn metric parameters from given data were proposed recently. In this contribution, we investigate a robust learning scheme to adapt metric parameters such as the scoring matrix in sequence alignment in conjunction with prototype learning, and we investigate the suitability of efficient approximations thereof.

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“…To adapt this measure to a given domain or learning task, the parameters (in this case the scoring of the alignment function) have to be adjusted accordingly. We proposed an approach for an autonomous adaptation of the scoring parameters, in conjunction with classifier training [18,16,17]. This facilitates model accuracy for the given solution space and also offers the possibility to interpret semantic implications of the adapted dissimilarity measure.…”