Multi-category classifiers and sample width
作者：
Highlights：
• The paper deals with learning large-margin multi-category classifiers.
• Instead of the usual functional-based definition of sample-margin, we use the notion of sample-width [4].
• Unlike in [4], classifiers map not simply from the real line, but from some metric space.
• We obtain PAC-like learning generalization-error bounds that involve the sample width. These are presented as two theorems.
• The results of this paper are applicable to machine learning, and have been used in [7] for learning case-based inference.
摘要
•The paper deals with learning large-margin multi-category classifiers.•Instead of the usual functional-based definition of sample-margin, we use the notion of sample-width [4].•Unlike in [4], classifiers map not simply from the real line, but from some metric space.•We obtain PAC-like learning generalization-error bounds that involve the sample width. These are presented as two theorems.•The results of this paper are applicable to machine learning, and have been used in [7] for learning case-based inference.
论文关键词：Multi-category classification,Generalization error,Machine learning,Pattern recognition
论文评审过程：Received 31 May 2015, Revised 21 April 2016, Accepted 23 April 2016, Available online 23 June 2016, Version of Record 15 July 2016.
论文官网地址：https://doi.org/10.1016/j.jcss.2016.04.003
