 
| Mime-Version: |
1.0 |
| Sender: |
|
| Date: |
Wed, 25 Jun 2003 10:52:51 +0000 |
| Reply-To: |
|
| Content-Transfer-Encoding: |
8bit |
| Subject: |
|
| From: |
|
| Content-Type: |
text/plain; charset=ISO-8859-1 |
| In-Reply-To: |
|
| Organization: |
IEETA, UNIVERSIDADE de AVEIRO |
| Comments: |
|
| Parts/Attachments: |
|
|
Many thanks for your pointer. I will find whether there is such a book
in our library.
On Wed, 2003-06-25 at 07:53, Gilles CARAUX wrote:
> If prior probabilities are unknown, I suggest MinMax rule as described in Hand (1981) page 7.
> The Bayesian decision rule, in MinMax approach, is designed so that we minimize the maximum possible risk.
>
> Hand, D. J. (1981), Discrimination and Classification, John Wiley.
>
>
> A 21:29 23/06/2003, qhwang a écrit :
> >Hi folks,
> >
> >Can anyone explain me about how to determine the priori probabilities when Bayesian decision rule is applied, say, to classify a pattern into object class and non-object class? I know usually equal priori is assumed, but in this case how can the training set for both classes be interpreted? I mean, are these priori probabilities not necessarily derived from the training set since usually in which postive samples are not equal to negative samples? Any help will be greatly appreciated.
> >
> >Many Thanks.
>
|
|
|
