|
UWEETR-2008-0005 Author(s): Keywords: Abstract This report reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and the most comprehensive set of experimental results to date. Specifically, the generalizability of using similarities as features is analyzed, design goals and methods for weighting nearest-neighbors for similarity-based learning are proposed, and different methods for consistently converting similarities into kernels are compared. Experiments on eight real data sets compare eight approaches and their variants to similarity-based learning. |