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From: Greg Heath <heath@alumni.brown.edu>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Pattern Classification using Neural Network ( newff)
Date: Sun, 8 Nov 2009 03:23:30 -0800 (PST)
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On Nov 8, 5:59 am, Greg Heath <he...@alumni.brown.edu> wrote:
> On Nov 2, 10:08 pm, "ade77 " <ade1...@gmail.com> wrote:
>
>
>
>
>
> > I did exactly the same project last year. Your code seems correct. I will assume the following based on the description of the problem:
>
> > 1. You have 20 rows of input features.
> > 2. You need to classify the problem into 4 possible outcomes.
> > unless you need to pass the weights into another program, the codes you have written:
> >  W = net.IW{1};> V = net.LW{2};
>
> > > W =W';
> > > V = V'
>
> > is completely unnecessay, since MATLAB will stote the weights in the network(net).
>
> > Back to the problem, once you have the network trained, all you have to do is test the network.
>
> > test = sim(net,new_input).
> > The trick here is that your output will produce 4 elements, the one that is closest to 1 is your classification.
>
> No.
>
> The maximum output is the winner.
>
> > For example if your classification is [red green blue orange], and you get
> > [1.2  2.4  5.6  3] , then red is your classification. because 1.2 is closest to 1
>
> No.
>
> The correct class is 3.
>
> That is why it is better to use LOGSIG. Then you can choose
> take the maximum probability( or maximum risk by considering
> prior probabilities and misclassification costs ...See
> the NN book by Duda et al ).

Then you can choose the class with the maximum posterior
probability( or minimum risk by considering prior probabilities
and misclassification costs ...See the NN book by Duda et al ).

Hope this helps.

Greg