> I'm using a Learning Vector Quantization network (LVQ) to classify data collected for deep brain stimulation.
Why are you using LVQ instead of a universal approximator (e.g., patternnet or newpr?)
> The training data set size is 70X69 and target size 2X68. my code body is :
70X69? Did you investigate input dimensionality reduction?
>net = newlvq(minmax(ptr),10,[.5 .5]);
>net.trainParam.epochs=50;
newlvq is doubly obsolete. What NNTBX version do you have? What is the reason for overwriting the defaults of numhidden = 20 and maxepochs = 1000?
>net = train(net,ptr, ttr);
>trout = sim(net,ptr);
>perftrain = perform(net,trout,ttr);
>etrain=ttr-trout;
>msetrain= mse(etrain);
Isn't msetrain == perftrain?
> Now the problem is , I could not get accuracy error more than 60% .
How did you calculate "accuracy error???
> I am using 100 repeated 10 fold cross validation. Also, Is there anything wrong with the code?
The choice of parameter values may be the problem.
>How do you know how many neurons to use in the competitive layer? > How many epochs should you use?
Trial and error after using defaults.
>How do you know that the LVQ is trained well?
Low error rates on nontraining data.
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