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From Noise-Free to Noise-Tolerant and from On-line to Batch Learning (1995)
| Content Provider | CiteSeerX |
|---|---|
| Author | Klasner, Norbert Simon, Hans Ulrich |
| Description | A simple method is presented which, loosely speaking, virtually removes noise or misfit from data, and thereby converts a "noise-free" algorithm A, which on-line learns linear functions from data without noise or misfit, into a "noise-tolerant" algorithm A nt which learns linear functions from data containing noise or misfit. Given some technical conditions, this conversion preserves optimality. For instance, the optimal noise-free algorithm B of Bernstein from [3] is converted into an optimal noisetolerant algorithm B nt . The conversion also works properly for all function classes which are closed under addition and contain linear functions as a subclass. In the second part of the paper, we show that Bernstein's on-line learning algorithm B can be converted into a batch learning algorithm B which consumes an (almost) minimal number of random training examples. This is true for a whole class of "pac-style" batch learning models (including learning with an (ffl; fl)- good model... In Proceedings of the Eighth Annual Conference on Computational Learning Theory |
| File Format | |
| Language | English |
| Publisher | ACM Press |
| Publisher Date | 1995-01-01 |
| Publisher Department | Fachbereich Informatik |
| Access Restriction | Open |
| Subject Keyword | Linear Function Whole Class Function Class Optimal Noisetolerant Algorithm Nt Minimal Number Optimal Noise-free Algorithm Second Part On-line Learns Linear Function Simple Method Random Training Example Good Model Technical Condition Contain Linear Function On-line Learning Algorithm Pac-style Batch Learning Model |
| Content Type | Text |
| Resource Type | Article |
