Generalization of metric classification algorithms for sequences classification and labelling

10/15/2016
by   Roman Samarev, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/27/2020

The complexity of L(p,q)-Edge-Labelling

We classify the complexity of L(p,q)-Edge-k-Labelling in the sense that ...
research
06/11/2013

Efficient Classification for Metric Data

Recent advances in large-margin classification of data residing in gener...
research
03/02/2010

Scalable Large-Margin Mahalanobis Distance Metric Learning

For many machine learning algorithms such as k-Nearest Neighbor (k-NN) c...
research
07/23/2012

Generalization Bounds for Metric and Similarity Learning

Recently, metric learning and similarity learning have attracted a large...
research
01/28/2016

Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss

Recent works on deep conditional random fields (CRF) have set new record...
research
10/26/2009

Parallelization of the LBG Vector Quantization Algorithm for Shared Memory Systems

This paper proposes a parallel approach for the Vector Quantization (VQ)...
research
11/21/2021

Multiscale entropic regularization for MTS on general metric spaces

We present an O((log n)^2)-competitive algorithm for metrical task syste...

Please sign up or login with your details

Forgot password? Click here to reset