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Myths and legends in learning classification rules (Document No: 19930004178)
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Buntine, Wray |
| Copyright Year | 1990 |
| Description | This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally. |
| File Size | 809091 |
| Page Count | 14 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_19930004178 |
| Archival Resource Key | ark:/13960/t49p7zb3r |
| Language | English |
| Publisher Date | 1990-05-01 |
| Access Restriction | Open |
| Subject Keyword | Cybernetics Classifications Algorithms Learning Theory Searching Machine Learning Bayes Theorem Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
