Multi-category classifiers and sample width

被引：4

作者：

Anthony, Martin ^{[1
]}

Ratsaby, Joel ^{[2
]}

机构：

[1] London Sch Econ & Polit Sci, Dept Math, Houghton St, London WC2A 2AE, England

[2] Ariel Univ, Elect & Elect Engn Dept, IL-40700 Ariel, Israel

来源：

JOURNAL OF COMPUTER AND SYSTEM SCIENCES | 2016年 / 82卷 / 08期

关键词：

Multi-category classification; Generalization error; Machine learning; Pattern recognition; UNIFORM-CONVERGENCE;

D O I：

10.1016/j.jcss.2016.04.003

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In a recent paper, the authors introduced the notion of sample width for binary classifiers defined on the set of real numbers. It was shown that the performance of such classifiers could be quantified in terms of this sample width. This paper considers how to adapt the idea of sample width so that it can be applied in cases where the classifiers are multi category and are defined on some arbitrary metric space. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：1223 / 1231

页数：9

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