Generalization Versus Classification
Abstract
Generalization is a learning problem that has received considerable attention. The generalization problem is to take a finite sample of some concept and produce an algorithm that can produce all other (perhaps infinitely many) samples of the same concept. Inductive inference is the study of this problem in a most general framework [1]. The classification problem is to take a finite sample of some concept and decide which type of concept the sample is from. The choice of type is usually finite. If the mechanism performing the classification is limiting, e.g., it makes more and more conjectures as to a classification as time goes on, then the process can also be considered as a type of learning. Roughly, we will say that some suitable mechanism has learned an appropriate classification if its sequence of conjectures stabilizes at some point. In this paper we formalize, at a suitable level of abstraction, the classification problem and rigorously compare it to the generalization problem. Despite some obvious similarities, the two notions are shown to be distinct. The new formalism of classification is investigated further.
Cite
Text
Wiehagen and Smith. "Generalization Versus Classification." Annual Conference on Computational Learning Theory, 1992. doi:10.1145/130385.130410Markdown
[Wiehagen and Smith. "Generalization Versus Classification." Annual Conference on Computational Learning Theory, 1992.](https://mlanthology.org/colt/1992/wiehagen1992colt-generalization/) doi:10.1145/130385.130410BibTeX
@inproceedings{wiehagen1992colt-generalization,
title = {{Generalization Versus Classification}},
author = {Wiehagen, Rolf and Smith, Carl H.},
booktitle = {Annual Conference on Computational Learning Theory},
year = {1992},
pages = {224-230},
doi = {10.1145/130385.130410},
url = {https://mlanthology.org/colt/1992/wiehagen1992colt-generalization/}
}