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. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Mokbel, B., Paassen, B., & Hammer, B. (2014). Efficient adaptation of structure metrics in prototype-based classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 571–578). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_72
Mendeley helps you to discover research relevant for your work.