Generalization of metric classification algorithms for sequences classification and labelling
Machine Learning
2016-12-19 v2 Computation and Language
Abstract
The article deals with the issue of modification of metric classification algorithms. In particular, it studies the algorithm k-Nearest Neighbours for its application to sequential data. A method of generalization of metric classification algorithms is proposed. As a part of it, there has been developed an algorithm for solving the problem of classification and labelling of sequential data. The advantages of the developed algorithm of classification in comparison with the existing one are also discussed in the article. There is a comparison of the effectiveness of the proposed algorithm with the algorithm of CRF in the task of chunking in the open data set CoNLL2000.
Cite
@article{arxiv.1610.04718,
title = {Generalization of metric classification algorithms for sequences classification and labelling},
author = {Roman Samarev and Andrey Vasnetsov and Elizaveta Smelkova},
journal= {arXiv preprint arXiv:1610.04718},
year = {2016}
}