An empirical study on improving dissimilarity-based classifications using one-shot similarity measure ?
We study enhancing the classification accuracy of dissimilarity-based classifications (DBC). The study is done empirically when measuring the dissimilarity with one-shot similarity (OSS). Two DBC approaches using the Euclidean distance and the OSS distance measures are compared. The latter, albeit not always, enhances the classification accuracy for certain kinds of data.
dissimilarity-based classification (dbc) statistical pattern recognition semi-supervised learning (ssl) one-shot similarity (oss) measure