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# Thread Subject: Neural Networks and Crossvalind

 Subject: Neural Networks and Crossvalind From: Greg Heath Date: 16 Jan, 2013 22:32:08 Message: 1 of 1 From: xristos lymbe Sent: Jan 14, 2013 06:39:06 AM >your answer was helpful thank you a lot! ... anyway some key words in your >answer lead me to find about "cross validation" and "leave one out", >so i made some progress > >indices=crossvalind('Kfold',y,5); >for i = 1:10 > testData = (indices == i); > trainData = ~testData;  > >now i wanna implement that the perceptron Nomenclature: feedforward network (preferred) or MLP (multi-layer-perceptron) >will use trainData to learn and testData to validate , Validation and testing are two very separate concepts. See my comments in the post http://www.mathworks.com/matlabcentral/answers/58761-a-complex-neural-network-data-partition-validation-subnetworks >smth like ...[net,tr] = train(net,trainData,targets); (any ideas?) Validation and testing are defaults. see the documentation, run the documentation examples. Then modify to fit yor purposes. Unfortunately, I don't think that the NNTBX works well with crossvalind. It is sufficient to use network functions net.divideFcn, net.divideParam and >> lookfor divide -----SNIP divideind - Partition indices into three sets using specified indices. divideint - Partition indices into three sets using interleaved indices. dividerand - Partition indices into three sets using random indices. dividetrain - Partition indices into training set only. dividevec - Divide problem vectors into training, validation and test vectors. >i am new to these things and i need them for my research in neuroscience, >the fact that i am not good in matlab keeps me really behind but i guess i >will improove since i started using it last month. >** after i finish the code i will make a post to mathworks.com becouse >many people have problems with these kind of stuff Inefficient. Post whenever you have a question or elucidating comment Greg

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