Cookies?
Library Header Image
LSE Research Online LSE Library Services

Large-width machine learning algorithm

Anthony, Martin and Ratsaby, Joel (2020) Large-width machine learning algorithm. Progress in Artificial Intelligence, 9 (3). 275 – 285. ISSN 2192-6360

[img] Text (Large-width machine learning algorithm) - Accepted Version
Download (302kB)

Identification Number: 10.1007/s13748-020-00212-4

Abstract

We introduce an algorithm, called Large Width (LW), that produces a multi-category classifier (defined on a distance space) with the property that the classifier has a large ‘sample width.’ (Width is a notion similar to classification margin.) LW is an incremental instance-based (also known as ‘lazy’) learning algorithm. Given a sample of labeled and unlabeled examples, it iteratively picks the next unlabeled example and classifies it while maintaining a large distance between each labeled example and its nearest-unlike prototype. (A prototype is either a labeled example or an unlabeled example which has already been classified.) Thus, LW gives a higher priority to unlabeled points whose classification decision ‘interferes’ less with the labeled sample. On a collection UCI benchmark datasets, the LW algorithm ranks at the top when compared to 11 instance-based learning algorithms (or configurations). When compared to the best candidate from instance-based learners, MLP, SVM, decision tree learner (C4.5) and Naive Bayes, LW is ranked at second place after only MLP which comes at first place by a single extra win against LW. The LW algorithm can be implemented in parallel distributed processing to yield a high speedup factor and is suitable for any distance space, with a distance function which need not necessarily satisfy the conditions of a metric.

Item Type: Article
Official URL: https://www.springer.com/journal/13748
Additional Information: © 2020 Springer-Verlag GmbH Germany, part of Springer Nature
Divisions: Mathematics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 20 Jul 2020 08:21
Last Modified: 20 Aug 2021 02:39
URI: http://eprints.lse.ac.uk/id/eprint/105746

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics