Labeled Compression Schemes for Extremal Classes

被引:15
作者
Moran, Shay [1 ,2 ,3 ]
Warmuth, Manfred K. [4 ]
机构
[1] Technion Israel Inst Technol, IL-32000 Haifa, Israel
[2] Microsoft Res, Herzliyya, Israel
[3] Max Planck Inst Informat, Saarbrucken, Germany
[4] Univ Calif Santa Cruz, Dept Comp Sci, Santa Cruz, CA 95064 USA
来源
ALGORITHMIC LEARNING THEORY, (ALT 2016) | 2016年 / 9925卷
关键词
SAMPLE COMPRESSION; LEARNABILITY; DIMENSION; SETS;
D O I
10.1007/978-3-319-46379-7_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is a long-standing open problem whether there exists a compression scheme whose size is of the order of the Vapnik-Chervonienkis (VC) dimension d. Recently compression schemes of size exponential in d have been found for any concept class of VC dimension d. Previously, compression schemes of size d have been given for maximum classes, which are special concept classes whose size equals an upper bound due to Sauer-Shelah. We consider a generalization of maximum classes called extremal classes. Their definition is based on a powerful generalization of the Sauer-Shelah bound called the Sandwich Theorem, which has been studied in several areas of combinatorics and computer science. The key result of the paper is a construction of a sample compression scheme for extremal classes of size equal to their VC dimension. We also give a number of open problems concerning the combinatorial structure of extremal classes and the existence of unlabeled compression schemes for them.
引用
收藏
页码:34 / 49
页数:16
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