Options
2015
Journal Article
Titel
Polynomial approximation of spectral data in LVQ and relevance learning
Abstract
High dimensional data serves as input for a variety of classification tasks. In the case of spectral information, this data can be understood as discrete sampling of an (unknown) underlying function. In this paper we discuss an approach that improves classification performance for spectral data by expanding the data in terms of basis functions. Two real world spectral data classification problems demonstrate the advantages of the method.