One-Class classifier using Neural Network
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Hi all. I'm having a problem setting up a proper Neural Network for one class classification. Basically I've only the features that rapresent a background of an image. So the training phase would train the NN on those features. During the execution phase the NN will have features that could be "background" or "foreground" (the upper step is segmentation, I've already done it). How I'm supposed to set up correctly my NN?
Here is some piece of code:
toTrainFeat = computeFeatures(backBboxes,frame);
classes(1:size(backBboxes,1))=1; % one-class
[net Y E] = adapt(net,toTrainFeat,classes); % Incremental learning
if numFrame >=40 || sse(E) <0.01 %classify only after 40 frames OR if NN is smart enough
y = net(toClassifyFeat);
y
end
This code does not work, I think because I'm submitting only ONE-CLASS to the adapt method (in fact it crashes when it call adapt). Any help? Thanks a lot.
采纳的回答
Greg Heath
2016-10-15
0 个投票
Use an RBF or EBF net. Generate points in between and outside of the clusters obtained from the original data.
Train the net with original data (1) and the simulated data outside and between clusters(0).
Hope this helps.
Thank you for formally accepting my answer
Greg
3 个评论
Leox91
2016-10-15
Greg Heath
2016-10-16
There is no reason why it should not work if the classes do not overlap and you have enough hidden nodes.
I did not say it would give better results. Just that the results will be easier to understand.
If you use RADBAS hidden nodes and the usual training functions, you will get elliptical basis functions because there will be no constraints to make them circular.
Hope this helps.
Greg
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