Are two Classifiers performing equally? A treatment using Bayesian Hypothesis Testing
| Type of publication: | Idiap-RR |
| Citation: | barber:hypothesis:rr:04:57 |
| Number: | Idiap-RR-57-2004 |
| Year: | 2004 |
| Month: | 5 |
| Institution: | IDIAP |
| Address: | Rue de Simplon 4, Martigny, CH-1920, Switerland |
| Note: | IDIAP-RR 04-57 |
| Abstract: | We consider here how to assess if two classifiers, based on a set of test error results, are performing equally well. This question is often considered in the realm of sampling theory, based on classical hypothesis testing. Here we present a simple Bayesian treatment that is quite general, and also is able to deal with the (practically common) case where the errors that two classifiers make are dependent. |
| Userfields: | ipdmembership={learning}, |
| Keywords: | |
| Projects |
Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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