Learning bounds via sample width for classifiers on finite metric spaces-Reference-Cited by-同舟云学术

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Learning bounds via sample width for classifiers on finite metric spaces

Published:2014-04 Issue: Volume:529 Page:2-10
ISSN:0304-3975
Container-title:Theoretical Computer Science
language:en
Short-container-title:Theoretical Computer Science

Author:

Anthony Martin,Ratsaby Joel

Funder

IST Programme of the European Community

Publisher

Elsevier BV

Subject

General Computer Science,Theoretical Computer Science

Reference25 articles.

1. Function learning from interpolation;Anthony;Combinatorics, Probability, and Computing,2000

2. Neural Network Learning: Theoretical Foundations;Anthony,1999

3. Maximal width learning of binary functions;Anthony;Theoretical Computer Science,2010

4. The performance of a new hybrid classifier based on boxes and nearest neighbors;Anthony,2012

5. Using boxes and proximity to classify data into several categories;Anthony,2012

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Learning half-spaces on general infinite spaces equipped with a distance function;Information and Computation;2023-03

2. Large-width machine learning algorithm;Progress in Artificial Intelligence;2020-08-05

3. Large width nearest prototype classification on general distance spaces;Theoretical Computer Science;2018-08

4. Large-width bounds for learning half-spaces on distance spaces;Discrete Applied Mathematics;2018-07

5. Classification based on prototypes with spheres of influence;Information and Computation;2017-10

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