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Abstract: . Within the framework of pac-learning, we explore the learnability of concepts from samples using the paradigm of sample compression schemes. A sample compression scheme of size k for a concept class C ` 2 X consists of a compression function and a reconstruction function. The compression function receives a finite sample set consistent with some concept in C and chooses a subset of k examples as the compression set. The reconstruction function forms a hypothesis on X from a compression set... (Update)
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...at most k = O(1) examples and O(log log n) additional bits. The notion of space bounded learning appears, for example, in [AFHM93, Ame94, Ame95, Flo89, FW95]. In addition the set of hypotheses used by A must have a VC dimension of O(1) this implies in particular that VC...
...scheme for a class. We shall discuss several types of compression schemes, in the spirit of the schemes discussed by Floyd and Warmuth [FW95], each of these schemes gives rise to its own parameter) ffl The optimal mistake bound for learning the class online (sometimes called...
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BibTeX entry: (Update)
Sally Floyd and Manfred Warmuth, "Sample Compression, learnability, and the Vapnik-Chervonenkis Dimension," Machine Learning, 21, 269--304 (1995). http://citeseer.ist.psu.edu/article/floyd95sample.html More
@article{ floyd95sample,
author = "Sally Floyd and Manfred K. Warmuth",
title = "Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension",
journal = "Machine Learning",
volume = "21",
number = "3",
pages = "269-304",
year = "1995",
url = "citeseer.ist.psu.edu/article/floyd95sample.html" }
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