Similarity-Based Classification: Concepts and Algorithms
Abstract
This paper reviews and extends the field of similarity-based classification, presenting new analyses, algorithms, data sets, and a comprehensive set of experimental results for a rich collection of classification problems. 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.
Cite
Text
Chen et al. "Similarity-Based Classification: Concepts and Algorithms." Journal of Machine Learning Research, 2009.Markdown
[Chen et al. "Similarity-Based Classification: Concepts and Algorithms." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/chen2009jmlr-similaritybased/)BibTeX
@article{chen2009jmlr-similaritybased,
title = {{Similarity-Based Classification: Concepts and Algorithms}},
author = {Chen, Yihua and Garcia, Eric K. and Gupta, Maya R. and Rahimi, Ali and Cazzanti, Luca},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {747-776},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/chen2009jmlr-similaritybased/}
}